Reviewerly/exHarmony huggingface dataset:
A DWT-SVD based adaptive color multi-watermarking scheme for copyright protection using AMEF and PSO-GWO

Color multi-watermarking is a challenge in the field of copyright protection, and the key in the color multi-watermarking technology is how to determine the optimal embedding regions and embedding strengths to achieve the trade-off among multiple watermarks while maintaining great invisibility, sufficient robustness, and large capacity. In order to tackle these problems, an adaptive multiple embedding factors (AMEF) algorithm for calculating the optimal embedding regions and the optimal embedding strengths is proposed in this paper to embed multiple color watermarks simultaneously. In the presented AMEF algorithm, we propose that the optimal embedding regions are determined by the contrast function of different blocks. Then we determine the multiple embedding strengths to depend on the weighted ratio of the contrast values of blocks and the eigenvalues of different marks. Furthermore, in order to calculate the weight value in the AMEF algorithm, we define a single objective function and utilize a hybrid particle swarm optimization and grey wolf optimizer (PSO-GWO) algorithm to optimize the objective function. In this work, by the use of the discrete wavelet transform (DWT), singular value decomposition (SVD) and AMEF, four encrypted color watermarks are inserted into the selected regions of the color (normal or medical) host image, simultaneously. Then watermarked host image is tested under various attacks and compared to other recent existing schemes. The experimental results demonstrate that the proposed scheme can effectively achieve the trade-off among the invisibility, robustness and capacity, simultaneously. And from the comparison results, the proposed scheme possesses high security, large capacity, and strong robustness against various attacks while maintaining good invisibility.
Blockchain and quantum blind signature-based hybrid scheme for healthcare 5.0 applications

Insurance agencies and digitally recorded healthcare databases can help society to decrease the high-level complexity and the cost of the entire healthcare ecosystem. The general data protection regulation provides the right to its data owner’s to know how data is stored, and for which purpose his/her data is being used. However, the healthcare data flow through an open channel, i.e., the Internet, which opens the doors for intruders to perform some malicious activities, such as breach of confidential data, modification in the stored data, etc. So it is a challenging task for the traditional healthcare systems to maintain the security and privacy of the stakeholders. Blockchain is emerged as a technology that improves the efficiency of today’s healthcare system and also helps to maintain the security and privacy of all the stakeholders. Motivated by these facts, in this paper, we analyse various security architectures used to secure electronic health records (EHRs) and apply Quantum Computing (QC) to the traditional encryption system. Then, we propose a blockchain-based architecture for Healthcare, which allows users to access the data from the database with their defined role. Further, to protect the traditional encryption system from the quantum attacks, the Quantum blind signature is used during the block creation using hyperledger Fabric blockchain. Results show the efficacy of the proposed scheme compared to the state-of-the-art schemes in terms of transaction throughput, resource consumption, and network traffic.
Combating Selection Biases in Recommender Systems with a Few Unbiased Ratings

Recommendation datasets are prone to selection biases due to self-selection behavior of users and item selection process of systems. This makes explicitly combating selection biases an essential problem in training recommender systems. Most previous studies assume no unbiased data available for training. We relax this assumption and assume that a small subset of training data is unbiased. Then, we propose a novel objective that utilizes the unbiased data to adaptively assign propensity weights to biased training ratings. This objective, combined with unbiased performance estimators, alleviates the effects of selection biases on the training of recommender systems. To optimize the objective, we propose an efficient algorithm that minimizes the variance of propensity estimates for better generalized recommender systems. Extensive experiments on two real-world datasets confirm the advantages of our approach in significantly reducing both the error of rating prediction and the variance of propensity estimation.
A double-blockchain solution for agricultural sampled data security in Internet of Things network

The security of Internet of Things (IoT) devices' sampled data in Agriculture is important as any malicious tampering or destroy may lead to wrong decision making and causes immeasurable losses. On the other hand, the emerging blockchain technology provides safer solutions in data protection and retracing. Yet when blockchain technology applied in agricultural data tracking and storage, the identity based searching is long and difficult. Also, public blockchain or consortium blockchain technology along cannot provide satisfactory solutions balancing between safety and high system performance. In this paper, we propose an Inter Planetary File System (IPFS) storage based double-blockchain solution for agricultural sampled data protection in IoT network. We use IPFS network to store the content of sampled data; the proposed system can retrieve the complete data segment by oracle mechanism. Then we design a consortium blockchain, Agricultural Sample Data Chain (ASDC), based on Ethereum technology and improve Merkle Patricia Trie (MPT) based account for every category of sampled data. Finally, after the data are stored in blocks in ASDC, block hash will be generated and upload on main chain of Ethereum to keep a public record in case malicious attacks occurs. Simulated results show that our proposed method has smaller time consumption than cloud storage and blockchain only storage; as well as our system is more robust than other two systems.
Predicting issue types on GitHub

Software maintenance and evolution involves critical activities for the success of software projects. To support such activities and keep code up-to-date and error-free, software communities make use of issue trackers, i.e., tools for signaling, handling, and addressing the issues occurring in software systems. However, in popular projects, tens or hundreds of issue reports are daily submitted. In this context, identifying the type of each submitted report (e.g., bug report, feature request, etc.) would facilitate the management and the prioritization of the issues to address. To support issue handling activities, in this paper, we propose Ticket Tagger, a GitHub app analyzing the issue title and description through machine learning techniques to automatically recognize the types of reports submitted on GitHub and assign labels to each issue accordingly. We empirically evaluated the tool's prediction performance on about 30,000 GitHub issues. Our results show that the Ticket Tagger can identify the correct labels to assign to GitHub issues with reasonably high effectiveness. Considering these results and the fact that the tool is designed to be easily integrated in the GitHub issue management process, Ticket Tagger consists in a useful solution for developers.
Robust feature learning for adversarial defense via hierarchical feature alignment

Deep neural networks have demonstrated excellent performance in most computer vision tasks in recent years. However, they are vulnerable to adversarial perturbations generated by adversarial attacks. These human-imperceptible perturbations often lead to severe distortion in the high-dimensional intermediate feature space, which is one of the major reasons for the vulnerabilities in deep neural networks. Therefore, input images with perturbations can completely change the predictions of the networks in the decision space. To overcome this drawback, we propose to progressively align the intermediate feature representations extracted from the adversarial domain with feature representations extracted from a clean domain through domain adaptation. The difference between two feature distributions can be accurately measured via an optimal transport-based Wasserstein distance. Thus, the deep networks are forced to learn robust and domain-invariant feature representations, so that the gap between the different domains is minimized and that the networks are no longer easily fooled by diverse adversaries. Extensive evaluations are conducted on four classification benchmark datasets in white-box attack scenarios. The evaluation results demonstrate a significant performance improvement over several state-of-the-art defense methods.
Short-term solar power forecasting: Investigating the ability of deep learning models to capture low-level utility-scale Photovoltaic system behaviour

Photovoltaic (PV) system power supply is characteristically intermittent. Therefore, PV forecasting is crucial for decision makers responsible for electrical grid stability. With forecast models traditionally trained as macro-level solutions, where a single model emulates the entire PV system, there is uncertainty regarding the ability of these macro-level models to capture the low-level power output dynamics of large multi-megawatt PV systems. Instead, an aggregated inverter-level forecasting methodology is proposed to obtain an enhanced forecasting accuracy. These macro-level and inverter-level forecasting methodologies are implemented with state-of-the-art deep learning based Feedforward neural network, Long Short-Term Memory and Gated Recurrent Unit recurrent neural network models. Results are generated for a real-world scenario, with multi-step forecasts delivered 1–6 h ahead for a 75 MW rated PV system. To ensure the scalability of the proposed methodology, a unique inverter-clustering technique is presented, which reduces the effort of optimising multiple low-level forecast models. A heuristic process of systematic hyperparameter optimisation is also proposed, which serves to guide future forecasting practitioners towards unbiased model development. From the deterministic and probabilistic confidence interval evaluations, overall results demonstrate a marginal increase in forecasting accuracy from the proposed aggregated inverter-level forecasts. The best performing macro-level model obtained Mean Absolute Percentage Error (MAPE) values ranging between 1.42%–8.13% for all weather types and forecast horisons. In comparison, the equivalent inverter-level forecasts delivered MAPE values ranging from 1.27%–8.29%. Finally, it is concluded that deep learning based macro-level forecast models have a sufficient ability to capture low-level PV system behaviour.
Human-Centered Artificial Intelligence for Designing Accessible Cultural Heritage

This paper reviews the literature concerning technology used for creating and delivering accessible museum and cultural heritage sites experiences. It highlights the importance of the delivery suited for everyone from different areas of expertise, namely interaction design, pedagogical and participatory design, and it presents how recent and future artificial intelligence (AI) developments can be used for this aim, i.e.,improving and widening online and in situ accessibility. From the literature review analysis, we articulate a conceptual framework that incorporates key elements that constitute museum and cultural heritage online experiences and how these elements are related to each other. Concrete opportunities for future directions empirical research for accessibility of cultural heritage contents are suggested and further discussed.
On the Impact of Programming Languages on Code Quality

This paper is a reproduction of work by Ray et al. which claimed to have uncovered a statistically significant association between eleven programming languages and software defects in projects hosted on GitHub. First we conduct an experimental repetition, repetition is only partially successful, but it does validate one of the key claims of the original work about the association of ten programming languages with defects. Next, we conduct a complete, independent reanalysis of the data and statistical modeling steps of the original study. We uncover a number of flaws that undermine the conclusions of the original study as only four languages are found to have a statistically significant association with defects, and even for those the effect size is exceedingly small. We conclude with some additional sources of bias that should be investigated in follow up work and a few best practice recommendations for similar efforts.
Haze concentration adaptive network for image dehazing

Learning-based methods have attracted considerable interest in image dehazing. However, most existing methods are not well adapted to different hazy conditions, especially when dealing with the heavily hazy scene. There is often a significant amount of haze that remains in the images recovered by most methods. To address this issue, we propose an end-to-end Haze Concentration Adaptive Network (HCAN), including a pyramid feature extractor (PFE), a feature enhancement module (FEM), and a multi-scale feature attention module (MSFAM) for image dehazing. Specifically, PFE based on the feature pyramid structure leverages complementary features from different CNN layers to help the clear image prediction. Then, FEM fuses four kinds of images with different haze density (i.e., three recovered images in the FEM with light haze density, and the input hazy image with strong haze condition) to guide the network to adaptively perceive images under different haze conditions. Finally, MSFAM is designed under two principles, multi-scale structure and attention mechanism. It is used to help the network produce a clear image with more details, and ease the network training. Comprehensive experiments demonstrate that the proposed HCAN performs favorably against the state-of-the-art methods in terms of PSNR, SSIM, and visual effect. The results, per-trained models and code are available at https://github.com/TaoWangzj/HCAN.
Deep transfer learning baselines for sentiment analysis in Russian

Recently, transfer learning from pre-trained language models has proven to be effective in a variety of natural language processing tasks, including sentiment analysis. This paper aims at identifying deep transfer learning baselines for sentiment analysis in Russian. Firstly, we identified the most used publicly available sentiment analysis datasets in Russian and recent language models which officially support the Russian language. Secondly, we fine-tuned Multilingual Bidirectional Encoder Representations from Transformers (BERT), RuBERT, and two versions of the Multilingual Universal Sentence Encoder and obtained strong, or even new, state-of-the-art results on seven sentiment datasets in Russian: SentRuEval-2016, SentiRuEval-2015, RuTweetCorp, RuSentiment, LINIS Crowd, and Kaggle Russian News Dataset, and RuReviews. Lastly, we made fine-tuned models publicly available for the research community.
Transformer-based deep neural network language models for Alzheimer’s disease risk assessment from targeted speech

Abstract Background We developed transformer-based deep learning models based on natural language processing for early risk assessment of Alzheimer’s disease from the picture description test. Methods The lack of large datasets poses the most important limitation for using complex models that do not require feature engineering. Transformer-based pre-trained deep language models have recently made a large leap in NLP research and application. These models are pre-trained on available large datasets to understand natural language texts appropriately, and are shown to subsequently perform well on classification tasks with small training sets. The overall classification model is a simple classifier on top of the pre-trained deep language model. Results The models are evaluated on picture description test transcripts of the Pitt corpus, which contains data of 170 AD patients with 257 interviews and 99 healthy controls with 243 interviews. The large bidirectional encoder representations from transformers (BERT Large ) embedding with logistic regression classifier achieves classification accuracy of 88.08%, which improves the state-of-the-art by 2.48%. Conclusions Using pre-trained language models can improve AD prediction. This not only solves the problem of lack of sufficiently large datasets, but also reduces the need for expert-defined features.
Offline Multi-Action Policy Learning: Generalization and Optimization

As a result of digitization of the economy, more and more decision makers from a wide range of domains have gained the ability to target products, services, and information provision based on individual characteristics. Examples include selecting offers, prices, advertisements, or emails to send to consumers, choosing a bid to submit in a contextual first-price auctions, and determining which medication to prescribe to a patient. The key to enabling this is to learn a treatment policy from historical observational data in a sample-efficient way, hence uncovering the best personalized treatment choice recommendation. In “Offline Policy Learning: Generalization and Optimization,” Z. Zhou, S. Athey, and S. Wager provide a sample-optimal policy learning algorithm that is computationally efficient and that learns a tree-based treatment policy from observational data. In our quest toward fully automated personalization, the work provides a theoretically sound and practically implementable approach.
Applications of new hybrid algorithm based on advanced cuckoo search and adaptive Gaussian quantum behaved particle swarm optimization in solving ordinary differential equations

This article solves first and second order differential equations with initial and/or boundary conditions by transforming these equations into unconstrained/bound constrained optimization problems. In order to solve these problems, a hybrid algorithm based on advanced cuckoo search (CS) algorithm and adaptive Gaussian quantum behaved particle swarm optimization (AGQPSO) is proposed. The CS algorithm is modified first by changing the step size in the simplified version. After that half of the total population is upgraded by this modified CS algorithm and another half is upgraded by AGQPSO algorithm. Then deletion strategy of CS algorithm is applied on the whole updated population. Next, to test the performance of the proposed hybrid algorithm, a number of benchmarks bound constrained optimization problems with different dimensions are considered and solved. Then this algorithm is applied fruitfully in first and second order initial value problems and boundary value problems by expressing the said problems in the form of bound constrained optimization problems.
Potentials and challenges of augmented reality smart glasses in logistics and supply chain management: a systematic literature review

This study investigates the potentials and challenges of applying augmented reality (AR) smart glasses in logistics and supply chain management (SCM). A systematic literature review on AR smart glasses was conducted to provide a comprehensive synthesis of what has been published in the literature to capture the dynamics surrounding this technology and identify areas deserving of further academic attention. To summarise the latest developments in this field, eighty-two (82) publications were selected and thoroughly analysed. In terms of research findings, four main clusters of potential benefits were identified: visualisation, interaction, user convenience, and navigation. In contrast, the challenges posed by smart glasses in the logistics field were clustered around technical, organisational, and ergonomic considerations. The accumulation of knowledge and actionable insights in this study will benefit both academics and practitioners interested in this emerging wearable technology segment. As the first attempt to explore the importance of smart glasses in logistics and SCM activities, this study offers significant contributions to the literature by codifying extant knowledge on AR smart glasses and setting forth an agenda for future research.
Toward a Quantum Software Engineering

Nowadays, we are at the dawn of a new age, the quantum era. Quantum computing is no longer a dream; it is a reality that needs to be adopted. But this new technology is taking its first steps, so we still do not have models, standards, or methods to help us in the creation of new systems and the migration of current ones. Given the current state of quantum computing, we need to go back to the path software engineering took in the last century to achieve the new golden age for quantum software engineering.
Hip-DE: Historical population based mutation strategy in differential evolution with parameter adaptive mechanism

Differential Evolution (DE) was a powerful population-based evolutionary algorithm for global optimization, and it achieved great success in both evolutionary computation competitions and engineering applications. Despite the excellent performance of the state-of-the-art DE variants, there are still two main weaknesses existing within them: one is the weakness in a given mutation strategy and the other is the weakness in the corresponding parameter control (of the mutation strategy). By reviewing the existing mutation strategies in the recent state-of-the-art DE variants, it can be seen that all of them have insufficient use of the knowledge obtained during the evolution because the historical information of the population is not taken into consideration, which inevitably leads to a bad perception of the landscapes of the objectives. Moreover, the adaptations of the control parameters including F and CR in these state-of-the-art DE variants are interlaced with one another. A bad F and a good CR may produce a good trial vector candidate, then the bad F is of misuse in the parameter control and vice versa. In this paper, a novel DE variant, called Hip-DE, meaning the latest fashion of DE, with historical population based mutation strategy was proposed to tackle the above mentioned weaknesses. Moreover, novel parameter adaptive mechanisms for control parameters F and CR as well as a platform based step-decrease scheme of population size were proposed to enhance capacity of the mutation strategy. By incorporating these three advancements, the novel Hip-DE algorithm secured an overall better performance on the tested benchmarks in comparison with the recent proposed state-of-the-art DE variants.
Data-Driven Estimation of Driver Attention Using Calibration-Free Eye Gaze and Scene Features

Driver attention estimation is one of the key technologies for intelligent vehicles. The existing related methods only focus on the scene image or the driver's gaze or head pose. The purpose of this article is to propose a more reasonable and feasible method based on a dual-view scene with calibration-free gaze direction. According to human visual mechanisms, the low-level features, static visual saliency map, and dynamic optical flow information are extracted as input feature maps, which combine the high-level semantic descriptions and a gaze probability map transformed from the gaze direction. A multiresolution neural network is proposed to handle the calibration-free features. The proposed method is verified on a virtual reality experimental platform that collected more than 550 000 samples and obtained a more accurate ground truth. The experiments show that the proposed method is feasible and better than the state-of-the-art methods based on multiple widely used metrics. This study also provides a discussion of the effects of different landscapes, times, and weather conditions on the performance.