Reviewerly/exHarmony huggingface dataset:
Neural scalarisation for multi-objective inverse reinforcement learning

Multi-objective inverse reinforcement learning (MOIRL) extends inverse reinforcement learning (IRL) to multi-objective problems by estimating weights and multi-objective rewards to help retrain and analyse preference-conditioned behaviour. Unlike previous methods using linear scalarisation, we propose a MOIRL method using neural scalarisation. This method comprises four neural networks: weight mapping, reward, scalarisation and weight back-translation. Additionally, we introduce two stabilization techniques for learning the proposed method. Experiments show that the proposed method can estimate appropriate weights and rewards reflecting true multi-objective intentions. Furthermore, the estimated weights and rewards can be used for retraining to reproduce the expert solutions.
The Design and Implementation of a Steganographic Communication System over In-Band Acoustical Channels

This article presents SoundSticker, a system for steganographic, in-band data communication over an acoustic channel. In contrast with recent works that hide bits in inaudible frequency bands, SoundSticker embeds hidden bits in the audible sounds, making them more reliably survive audio codecs and bandpass filtering, while achieving a higher data rate and remaining imperceptible to a listener. The key observation behind SoundSticker is that the human ear is less sensitive to the audio phase changes than the frequency and amplitude changes, which leaves us an opportunity to alter the phase of an audio clip to convey hidden information. We take advantage of this opportunity and build an OFDM-based physical layer. To make this PHY-layer design work for a variety of end devices with heterogeneous computation resources, SoundSticker addresses multiple technical challenges including perceivable waveform artifacts caused by the phase-based modulation, bit rate adaptation without channel sounding and real-time preamble detection. Our prototype on both smartphones and ESP32 platforms demonstrates SoundSticker’s superior performance against the state of the arts, while preserving excellent sound quality and remaining unaffected by common audio codecs like MP3 and AAC. Audio clips produced by SoundSticker can be found at https://soundsticker.github.io/ .
DBSDS: A dual‐blockchain security data sharing model with supervision and privacy‐protection

Abstract With the rapid development of big data technology and applications, sharing and supervision of massive data have become the demand of industry development. As an emerging technology, blockchain, with excellent characteristics, can promote the prosperity and development of the data‐sharing industry. However, the existing blockchain may lead to some privacy and security issues due to its transparency and lack of supervision. Hence, a secure data sharing model with supervision and privacy protection, named DBSDS, was proposed, which supports illegal content supervision and users revocation, and possesses flexible and fine‐grained access control on data sharing. Firstly, it introduces a dual‐blockchain architecture, one blockchain is used to ensure the integrity and traceability of private data and another is utilized to supervise illegal users so that they can no longer upload and share private data. Secondly, a key tracing tree was built and correspondingly different key generation strategies for different users were designed, which endows regulators with supervision capabilities. Finally, combined ABPER‐KS algorithm, both privacy protection and fine‐grained access control on private data can be accomplished. Furthermore, security analysis and performance comparison manifest that DBSDS is safe and owns better performance, and scheme implementation and experimental analysis indicate the practicality of DBSDS.
The role of educational channels in raising the level of learning among the students of the Kurdistan Region

This research aims to reveal the role of educational channels in raising the level of learning among the students of the Kurdistan Region and to know the percentage of their use and the importance of these channels for studentsn , The importance of the research for a clear vision of the educational channels lies in their awareness of the educational programs they offer and how they reflect on the level of student learning , for this purpose the researcher has done a thoughtful research through the distribution of the survey form ( questionnaire) to the students of grades (10,11 and 12) of the two sections (literary and scientific) for both genders , whose number (455) students in sulaymaniyah , to get their snswer , and in the end the researcher came to the conclusion that students benefited greatly from watching educational channels, the majority of students who use educational channels are female and have lower rates .
ConvNeXt-Haze: A Fog Image Classification Algorithm for Small and Imbalanced Sample Dataset Based on Convolutional Neural Network

Aiming at the different abilities of the defogging algorithms in different fog concentrations, this paper proposes a fog image classification algorithm for a small and imbalanced sample dataset based on a convolution neural network, which can classify the fog images in advance, so as to improve the effect and adaptive ability of image defogging algorithm in fog and haze weather. In order to solve the problems of environmental interference, camera depth of field interference and uneven feature distribution in fog images, the CutBlur-Gauss data augmentation method and focal loss and label smoothing strategies are used to improve the accuracy of classification. It is compared with the machine learning algorithm SVM and classical convolution neural network classification algorithms alexnet, resnet34, resnet50 and resnet101. This algorithm achieves 94.5% classification accuracy on the dataset in this paper, which exceeds other excellent comparison algorithms at present, and achieves the best accuracy. It is proved that the improved algorithm has better classification accuracy.
A Scheduling Method for Heterogeneous Signal Processing Platforms Based on Quantum Genetic Algorithm

Currently, many problems such as variable signal resources, complex execution environments, and low efficiency of scheduling algorithms are faced by heterogeneous signal processing platforms. The task scheduling algorithm is one of the key factors that directly affect the performance of the processing platform. In order to solve the problems of low efficiency of task scheduling algorithms and high computational cost of processors, a heterogeneous platform scheduling algorithm based on the quantum genetic algorithm is proposed in this paper. The algorithm constructs a task scheduling model by using a directed acyclic graph. This paper quantifies the mapping relationship between the quantum genetic algorithm and task scheduling. It corresponds qubits to binary, chromosomes to processor numbers, and individuals to processor scheduling strategies. In this paper, a new way of coding chromosomes using quantum coherence properties is designed to reduce the population size and increase population diversity. Crossover operations are performed on all individuals using full-interference crossover to avoid the results falling into local optimal solutions. The population of slow convergence is solved by implementing mutation operations on populations through quantum rotation gates. In addition, a task pre-ordering stage is designed based on the table scheduling algorithm. The task scheduling priority developed at this stage is used as the reference value for the initial encoding of the population, so that the search space for solutions is reduced. Finally, experiments are conducted using randomly generated task graphs. The algorithm is compared with improved genetic algorithms and existing intelligent scheduling algorithms. The results show that the algorithm can still obtain better results when the number of populations and iterations is small. It is more appropriate for heterogeneous platforms and computation-intensive tasks.
Stacked <scp>multi‐layer</scp> security for Hadoop distributed file system using <scp>HSCT</scp> steganography

Summary Big data needs to have scalable storage platforms as well as parallel processing environments. Hadoop is preferable because it is an open‐source software which works in a distributed, parallel processing environment and no need of any special hardware. In recent developments of Hadoop, security was introduced as optional. A symmetric block cipher advanced encryption standard (AES) is used for encryption. A 128 MB block of data needs 8 million encryptions. A better way of hiding the data from unauthorized access is preferable. Steganography, a more contemporary security technology than cryptography, is the art and science of hiding subconscious information in hidden item while exposing opponents their existence. The cover is an image, and the secret information may be a photo, music, video, or text. A double layer hexcode substituted–color space transformed (HSCT) steganography with 3 band splitting of data ensures confidentiality of the data and one time pad key generated with client's ID provides authentication also. Considering the volume of data, steganography concept is much faster than AES or any other cipher. Hiding the data inside the random images will indulge in fast reconstruction of data when needed for processing using either MapReduce or tools like spark.
CYBERATTACKS ON HEALTHCARE SYSTEMS

Cyberattacks on healthcare information systems have become more frequent in the last few years. The consequences are not only the collapse of a system and a financial damage, but also a direct threat to the patients' health and lives. The basic methods and techniques of stealing health data from end users of the internet and the use of stolen data to commit criminal acts are presented in this paper. Numerous cyberattacks on healthcare systems worldwide are reviewed and analyzed, with a focus on the COVID19 pandemic. In recent years, the international community has intensified its work on legal regulations and conventions and raising awareness concerning the cyber security of healthcare systems. Cyberattacks on healthcare systems are a global problem, and the solutions should be global, as well.
Exact test theory in Gaussian graphical models

In this paper, we derive several statistical tests on the precision matrix with application to the determination of the structure of an undirected Gaussian graph. The exact distributions of the test statistics are obtained under the null hypotheses, while the exact distributions of the random matrices, which are used in the construction of the test statistics, are deduced under the alternative hypothesis. Moreover, we present the high-dimensional asymptotic distributions of the test statistics under the null hypothesis. The testing problems that an undirected Gaussian graph possesses a structure that corresponds to the precision matrix of an AR(1) process, to the block-diagonal precision matrix and to the precision of a factor model are discussed in detail. The performance of the proposed statistical tests is further investigated via an extensive simulation study and compared to the benchmark approach.
Generating Paraphrase Using Simulated Annealing for Citation Sentences

The paraphrase generator for citation sentences is used to produce several sentence alternatives to avoid plagiarism. Furthermore, the generation results need to pay attention to semantic similarity and lexical divergence standards. This study proposed the StoPGEN model as an algorithm for generating citation paraphrase sentences with stochastic output. The generation process is guided by an objective function using a simulated annealing algorithm to maintain the properties of semantic similarity and lexical divergence. The objective function is created by combining the two factors that maintain these properties. This study combined METEOR and PINC Scores in a linear weighting function that can be adjusted for its value tendency in one of the matrix functions. The dataset of citation sentences that had been labeled with paraphrases was used to test StoPGEN and other models for comparison. The StoPGEN model, with the citation sentences dataset, produced a BLEU score of 55.37, outperforming the bidirectional LSTM method with a value of 28.93. StoPGEN was also tested using Quora data by changing the language source in the architecture section resulting in a BLEU score of 22.37, outperforming UPSA 18.21. In addition, the qualitative evaluation results of the citation sentence generation based on respondents obtained an acceptance value of 50.80.
A Brief Analysis of Smart Contracts and Applications of Blockchain Technology

Blockchain refers to a distributed system or ledger that is shared via a computer network. For storing data, a blockchain serves as a digital database. The most well-known use of Blockchain technology is to maintain a secure and impartial record of every transaction made using Bitcoin and other types of cryptocurrency. By assuring the security and integrity of a data record, a blockchain encourages confidence without the need for a trustworthy third party. This paper discusses the various aspects and implications of Blockchain technology in modern society.
Discussing the needs of high resolution data: their impact in evaluating solar potential considering the horizon height

The need for high-resolution data is exponentially growing in the current energy transition. In this sense, geographic information system tools are crucial for a more precise evaluation of solar energy potentials in complex urban environments. Thus, this work aims to provide novel insights into the need for high-resolution data and understand when and where these are required. The work analyses the impact of the horizon height of the surrounding areas as well as the more distant obstacles in evaluating the solar potential in three given locations in Bolzano, Italy starting from a high-resolution digital surface model. It was found that the more complex the location is, the more a spatially explicit approach with high-resolution data is required to detect the impact of the surrounding buildings. More importantly, it substantially affects the hourly availability of solar energy, which is crucial when simulating and dimensioning integrated systems at the building level.
Semi-Supervised Semantic Role Labeling with Bidirectional Language Models

The recent success of neural networks in NLP applications has provided a strong impetus to develop supervised models for semantic role labeling (SRL) that forego the requirement for extensive feature engineering. Recent state-of-the-art approaches require high-quality annotated datasets that are costly to obtain and almost unavailable for low-resource languages. We present a semi-supervised approach that utilizes both labeled and unlabeled data to provide performance improvement over a mere supervised SRL model. We show that our proposed semi-supervised SRL model provides larger improvement over a supervised model in the scenario where labeled training data size is small. Our SRL system leverages unlabeled data under the language modeling paradigm. We demonstrate that the incorporation of a self pre-trained bidirectional language model (S-PrLM) into a SRL system can help in SRL performance improvement by learning composition functions from the unlabeled data. Previous researches have concluded that syntax information is very useful for high-performing SRL systems, so we incorporate syntax information by employing an unsupervised approach to leverage dependency path information to connect argument candidates in vector space, which helps in distinguishing arguments with similar contexts but different syntactic functions. The basic idea is to connect predicate ( w p ) with argument candidate ( w a ) with the dependency path ( r ) between them in the embedding space. Experiments on the CoNLL-2008 and CoNLL-2009 datasets confirm that our full SRL model outperforms previous best models in terms of F 1 score.
Impact of restructuring resources and services and their utilization in IIT Roorkee library, India

Purpose This paper examines the impact of restructuring on the usage of resources and services in the Indian Institute of Technology library, Roorkee. Library users were approached to know their preferences between an electronic and traditional resources and services. Additionally, the study focused to know the extent of change in users' awareness, dependency, usage, frequency and satisfaction from library resources and services after restructuring these due to the availability of abundance of e-resources. Design/methodology/approach The study adopted survey-based research design, for which a questionnaire was used as the principal instrument for data collection. This questionnaire was distributed to the library users when they physically visited the library. Notably, all the questionnaires distributed were returned, yielding thereby a cent percent response rate. One questionnaire was administrative on the librarian for getting the data regarding the changes made in the subscription of e-resources and services. The collected data were further analyzed with appropriate statistical techniques of Statistical Package for Social Sciences (SPSS) as chi-square test, mean and standard deviation. Findings The results showed that the use of both restructured resources and services had increased, and was found to be helpful for users. Also, the users' dependency on library had increased, showing thereby an upward trend for usage in the future. However, although the users’ awareness, usage and frequency of e-resources and ICT-based services have positively changed, and partially been satisfied, the study suggests that further revision is needed for further strengthening e-resources and services as per user's needs satisfaction. Practical implications This study would create interest in rethinking library resources and services among librarians that tend to respond positively to user satisfaction and library functions; it also provides a strategic position of a library per se for its users in the future. Importantly, the assessment of the usage of restructuring resources and services, vis-a-vis their impact on users, may also serve as a key tool, which possibly could be applied to both public libraries and other types of higher education libraries. Originality/value Many libraries find it difficult to balance between electronic and printed materials, especially in the fields of science and technology, due to the preferences of the users. Thus far, there have not been too many studies that have looked to assess restructuring of library resources and services, vis-a-vis their impact on library users. Therefore, the results of this study would provide a clear understanding to the library profession as to how users use e-resources and services in the digital age, and how much they are actually influenced by these.
Research on the multimodal fusion calibration method of the binocular visual system in a large-scale FOV

Abstract Calibration is vital for the binocular visual system. The high-precision measurement volume of the traditional binocular visual system is very small; therefore, it is difficult to measure large objects accurately. Accurate calibration of the binocular visual system is also extremely difficult in a large-scale field-of-view (FOV). Several attempts have been made to calibrate the binocular visual system in a large-scale FOV without recognizing the importance of depth information. A new method based on multimodal constraints has been proposed to calibrate the binocular visual system in a large-scale FOV whose height-width-depth is 2*2*2 <?CDATA $\mathrm{m}^3$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msup> <mml:mrow> <mml:mi mathvariant="normal">m</mml:mi> </mml:mrow> <mml:mn>3</mml:mn> </mml:msup> </mml:math> . The method fuses multiple constraints, i.e. the distance error constraint, the reprojection error constraint, the epipolar error constraint, the spatial points error constraint, and the regularization constraint. An objective function that integrates the above constraints has been derived. The construction method of the global Jacobi matrix, the adaptive weight adjustment method, and the Levenberg–Marquardt iterative algorithm are used to minimize the objective function. Comparison experiments have been carried out to validate the performance of our proposed method by measuring a 1.4 m long calibration gauge. The results show that the root mean square (RMS) of the residual error of our method is 0.0929 mm, Zhang’s traditional method is 0.523 mm, and Xiao’s photogrammetric method is 0.348 mm. The RMS of our method is 82% less than that of Zhang’s method. Noise experiments show that the RMS of the residual error of our method is 45% less than Zhang’s method with 0.1 level noise inference, which verifies the stability and reliability of our method.
Rough-Fuzzy Based Synthetic Data Generation Exploring Boundary Region of Rough Sets to Handle Class Imbalance Problem

Class imbalance is a prevalent problem that not only reduces the performance of the machine learning techniques but also causes the lacking of the inherent complex characteristics of data. Though the researchers have proposed various ways to deal with the problem, they have yet to consider how to select a proper treatment, especially when uncertainty levels are high. Applying rough-fuzzy theory to the imbalanced data learning problem could be a promising research direction that generates the synthetic data and removes the outliers. The proposed work identifies the positive, boundary, and negative regions of the target set using the rough set theory and removes the objects in the negative region as outliers. It also explores the positive and boundary regions of the rough set by applying the fuzzy theory to generate the samples of the minority class and remove the samples of the majority class. Thus the proposed rough-fuzzy approach performs both oversampling and undersampling to handle the imbalanced class problem. The experimental results demonstrate that the novel technique allows qualitative and quantitative data handling.
Research on Wavelet Transform Modulus Maxima and OTSU in Edge Detection

During routine bridge maintenance, edge detection allows the partial condition of the bridge to be viewed. However, many edge detection methods often have unsatisfactory performances when dealing with images with complex backgrounds. Moreover, the processing often involves the manual selection of thresholds, which can result in repeated testing and comparisons. To address these problems in this paper, the wavelet transform modulus maxima method is used to detect the target image, and then the threshold value of the image can be determined automatically according to the OTSU method to remove the pseudo-edges. Thus, the real image edges can be detected. The results show that the information entropy and SSIM of the detection results are the highest when compared with the commonly used Canny and Laplace algorithms, which means that the detection quality is optimal. To more fully illustrate the advantages of the algorithms, images with more complex backgrounds were detected and the processing results of the algorithms in this paper are still optimal. In addition, the automatic selection of thresholds saves the operator’s effort and improves the detection efficiency. Thanks to the combined use of the above two methods, detection quality and efficiency are significantly improved, which has a good application in engineering practice.