Reviewerly/exHarmony huggingface dataset:
International Economic Institutions: Different Tasks And Risks of Repercussions at the Global Level

Purpose: International economic institutions occupy a great position at the level of global economies by providing great financial support to member countries. These institutions also seek to study their position at the international level and the extent of their capabilities to confront the crises that afflict developed and developing countries, as they have contributed to addressing the crises faced by the countries of the world. Since its inception and the repercussions and potential risks that threaten its economic stability. The study aims to achieve several goals, most notably studying the concept of international economic institutions and their implications for international economies, knowing the tasks of the International Monetary Fund and the World Bank regarding loan policies, and clarifying the implications and risks of loans provided by the Fund and the World Bank on the reality of global economies. Theoretical framework: To approve the hypothesis of the study and achieve its aims, a deductive approach is adopted starting from realistic constants in collecting data and facts, as well as adopting the descriptive analytical method in the study of international financial institutions. Design/methodology/approach: The impact of the chaos witnessed by the First and Second World Wars prompted the international financial institutions to assume the responsibility of managing the international monetary system and ensuring global economic stability through the application of policies to achieve this and address the deficit in the balance of payments of the member states of these institutions. Findings: The study hypothesized that despite the repercussions and risks of wars on developing and developed countries, the international financial institutions represented by the IMF and the world bank were able, through their tasks, to offer packages of loans to fill part of the deficit that these countries were exposed to, which enabled the countries to finance its economic projects. Research, Practical &amp; Social implications: The most prominent finding of the study is that what distinguishes the work of the international monetary fund from the world bank is the duration of time, as the fund provides short-term loans, unlike the world bank, which deals with long-term loans. Regarding the lending policy, despite the high-interest rates imposed on loans by international economic institutions, they are linked to structural measures that constitute an obstacle to the lending country. Originality/value: The problem of the study lies in the fact that most countries of the world, whether developed or developing, are exposed to many economic problems as a result of their lack of international financing needs to confront the crises they are exposed to, forcing them to resort to international financial institutions to fill the deficit.
An improved region-based embedding technique for data hiding and image recovery using multiple ROI and RONI

To preserve the sensitive contents of digital images during their transmission, it is essential to hide them with maximum imperceptibility so that intruders will not be able to identify them visually. The image recovery at the receiver end is equally significant because of sensitive images, like medical diagnosis images, satellite images, etc. This paper proposes an improved image hiding technique where the sensitive contents of the image are located in multiple regions. These regions get embedded based on histogram analysis of the region of interest (ROI) pixels then hidden in the region of non-interest (RONI). Further, the reverse operations can be applied to the embedded regions and the hidden data are retrieved from RONI. Using this technique, the embedded regions can be easily extracted, recovered, and fully restored without any loss in data. The proposed work is on multiple ROI with the reliability, integrity, and confidentiality of transmitted images.
Adversarial Attack on Communication Signal Modulation Recognition

Convolutional network models (CNN) are very vulnerable to adversarial samples, which poses a serious challenge to the security of CNN models. Based on the task of CNN's modulation and identification of communication signals, we propose a white-box attack algorithm, the shortest distance attack method (SD-Alg), which can generate extremely small disturbances and greatly reduce the classification performance of the model. Experiments show that our algorithm excels in attack success rate, running time and adversarial perturbation size among the same type of algorithms.
Speeding Up Multi-Scalar Multiplication over Fixed Points Towards Efficient zkSNARKs

The arithmetic of computing multiple scalar multiplications in an elliptic curve group then adding them together is called multi-scalar multiplication (MSM). MSM over fixed points dominates the time consumption in the pairing-based trusted setup zero-knowledge succinct non-interactive argument of knowledge (zkSNARK), thus for practical applications we would appreciate fast algorithms to compute it. This paper proposes a bucket set construction that can be utilized in the context of Pippenger’s bucket method to speed up MSM over fixed points with the help of precomputation. If instantiating the proposed construction over BLS12-381 curve, when computing n-scalar multiplications for n = 2e (10 ≤ e ≤ 21), theoretical analysis ndicates that the proposed construction saves more than 21% computational cost compared to Pippenger’s bucket method, and that it saves 2.6% to 9.6% computational cost compared to the most popular variant of Pippenger’s bucket method. Finally, our experimental result demonstrates the feasibility of accelerating the computation of MSM over fixed points using large precomputation tables as well as the effectiveness of our new construction.
Watching your call: Breaking VoLTE Privacy in LTE/5G Networks

Voice over LTE (VoLTE) and Voice over NR (VoNR), are two similar technologies that have been widely deployed by operators to provide a better calling experience in LTE and 5G networks, respectively. The VoLTE/NR protocols rely on the security features of the underlying LTE/5G network to protect users' privacy such that nobody can monitor calls and learn details about call times, duration, and direction. In this paper, we introduce a new privacy attack which enables adversaries to analyse encrypted LTE/5G traffic and recover any VoLTE/NR call details. We achieve this by implementing a novel mobile-relay adversary which is able to remain undetected by using an improved physical layer parameter guessing procedure. This adversary facilitates the recovery of encrypted configuration messages exchanged between victim devices and the mobile network. We further propose an identity mapping method which enables our mobile-relay adversary to link a victim's network identifiers to the phone number efficiently, requiring a single VoLTE protocol message. We evaluate the real-world performance of our attacks using four modern commercial off-the-shelf phones and two representative, commercial network carriers. We collect over 60 hours of traffic between the phones and the mobile networks and execute 160 VoLTE calls, which we use to successfully identify patterns in the physical layer parameter allocation and in VoLTE traffic, respectively. Our real-world experiments show that our mobile-relay works as expected in all test cases, and the VoLTE activity logs recovered describe the actual communication with 100% accuracy. Finally, we show that we can link network identifiers such as International Mobile Subscriber Identities (IMSI), Subscriber Concealed Identifiers (SUCI) and/or Globally Unique Temporary Identifiers (GUTI) to phone numbers while remaining undetected by the victim.
Automatic optimization of miniaturized bound states in the continuum cavity

Bound states in the continuum (BICs) provide, what we believe to be, a novel and efficient way for light trapping. However, using BICs to confine the light into a three-dimensional compact volume remains a challenging task, since the energy leakage at the lateral boundaries dominates the cavity loss when its footprint shrinks to considerably small, and hence, sophisticated boundary designs turn out to be inevitable. Conventional design methods fail in solving the lateral boundary problem because a large number of degree-of-freedoms (DOFs) are involved. Here, we propose a fully automatic optimization method to promote the performance of lateral confinement for a miniaturized BIC cavity. Briefly, we combine a random parameter adjustment process with a convolutional neural network (CNN), to automatically predict the optimal boundary design in the parameter space that contains a number of DOFs. As a result, the quality factor that is accounted for lateral leakage increases from 4.32 × 104 in the baseline design to 6.32 × 105 in the optimized design. This work confirms the effectiveness of using CNNs for photonic optimization and will motivate the development of compact optical cavities for on-chip lasers, OLEDs, and sensor arrays.
Aligning Visual and Lexical Semantics

We discuss two kinds of semantics relevant to Computer Vision (CV) systems - Visual Semantics and Lexical Semantics. While visual semantics focus on how humans build concepts when using vision to perceive a target reality, lexical semantics focus on how humans build concepts of the same target reality through the use of language. The lack of coincidence between visual and lexical semantics, in turn, has a major impact on CV systems in the form of the Semantic Gap Problem (SGP). The paper, while extensively exemplifying the lack of coincidence as above, introduces a general, domain-agnostic methodology to enforce alignment between visual and lexical semantics.
Efficiency Analysis of Jaccard Similarity in Probabilistic Distribution Model

The inner probabilistic properties of the big data have a great impact on the performance of pattern recognition systems.Jaccard similarity (JS) is a most popular statistic metric used for calculating the similarity of objects in feature extraction process.The paper combines JS with probabilistic distribution model to explore the effect of the inner properties of big data.It deduced the generalized form of JS for probabilistic model and determined the calculation method of JS for power-law and exponential distribution.Experiment observations showed that power-law distribution has higher JS than the correspondent exponential distribution, which denotes that power-law probabilistic structure is a more efficient probability structure.The original normalized data in MNIST database exhibited a more power-law-like distribution and the randomly translated data exhibited a more exponential-like distribution.The MNIST data with power-law-like property has higher JS and are more efficient comparing to the translated data.Thus, these observations provide possible guidelines for efficient information coding and processing methods.
Realistic problems and innovative path of AI dubbing in the era of intelligent media

The rapid development of artificial intelligence technology has led human beings into the era of intelligent media, and is inextricably linked with human social production and life.In the field of media, the implementation of AI technology brings technical dividends to the development of media industry, but it is accompanied by real problems brought by immature technology and increased audience demands.This paper addresses the problems of voice homogeneity, mechanical flow of speech, and lack of emotion in AI voiceover, and makes suggestions for its innovative development by combining theories related to the art of broadcasting and hosting.
Stroke-GAN Painter: Learning to paint artworks using stroke-style generative adversarial networks

Abstract It is a challenging task to teach machines to paint like human artists in a stroke-by-stroke fashion. Despite advances in stroke-based image rendering and deep learning-based image rendering, existing painting methods have limitations: they (i) lack flexibility to choose different art-style strokes, (ii) lose content details of images, and (iii) generate few artistic styles for paintings. In this paper, we propose a stroke-style generative adversarial network, called Stroke-GAN, to solve the first two limitations. Stroke-GAN learns styles of strokes from different stroke-style datasets, so can produce diverse stroke styles. We design three players in Stroke-GAN to generate pure-color strokes close to human artists’ strokes, thereby improving the quality of painted details. To overcome the third limitation, we have devised a neural network named Stroke-GAN Painter, based on Stroke-GAN; it can generate different artistic styles of paintings. Experiments demonstrate that our artful painter can generate various styles of paintings while well-preserving content details (such as details of human faces and building textures) and retaining high fidelity to the input images.
Anomaly Detection of Water Pollution Prevention Ecosystem Based on Artificial Intelligence

Traditional water pollution(WP) detection is mainly based on the manual use of water quality testing equipment to identify samples at fixed points, this method of detection is a huge amount of work, and to pay the high cost of testing, in addition, in some unsuitable for operators to enter the WP testing environment, the use of traditional testing equipment is difficult to complete the sampling, so that the testing work can not be carried out as scheduled, and, the current water resources collection every six months or once a quarter, in this way to collect water quality data on a long time line, can not do water quality status updated.In order to solve these problems and build a stable water environment ecosystem, this paper studies WP detection equipment, builds a WP detection system based on artificial intelligence and sensors, and integrates intelligent means to detect pollutants and prevent WP.The system can complete the abnormal detection and recording of water pollutants through different interfaces and software systems, and its advantages of miniaturization and intelligence will be applied to actual production and life.
Label Propagation With Contrastive Anchors for Deep Semi-Supervised Superheat Degree Identification in Aluminum Electrolysis Process

Accurate identification of multimodal Superheat Degree (SD) plays a critical decision-making role in Aluminum Electrolysis Process (AEP). Because the labeled SD data are scarce and annotation is expensive in real-world AEP, it is a challenge to develop a well-behaved SD identification model. In this paper, a Contrastive Anchors-based Label Propagation (CALP) algorithm is proposed to construct a Deep Semi-Supervised Learning (DSSL) model, called CALP-DSSL, for better identifying SD by utilizing large-scale unlabeled data and limited labeled data. Specifically, to improve the reliability of affinity graph and its affinity matrix, positive anchors and negative anchors are generated by estimating the uncertainty of label predictions, which can guide the correct direction of inferring pseudo-labels. For tackling the unsupervised domain adaptation problems existing in AEP, we propose a Variational Information Domain Adaptation (VIDA) module using the pseudo-labels generated by CALP to fine-tune the deep Variational Information Bottleneck (VIB) network. Finally, the overall CALP-DSSL model is trained by the Mini-Batch Incremental Learning (MBIL) technique in local level. It matches the nearest neighbors based on batch embedded features, which provides more distinct information flow during subsequent label propagation to construct the affinity graph. Benchmark datasets verify the superiority of CALP algorithm. Case study on a real-world AEP shows that CALP-DSSL model improves the accuracy of SD identification over other state-of-art DSSL methods. Our source code is available at https://github.com/wjiecsu/CALP-DSSL. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The focus of this paper is to develop a well-behaved SD identification model based on the improved deep semi-Supervised learning (CALP-DSSL) model. In this model, a Contrastive Anchors based-Label Propagation (CALP) algorithm is used to predict pseudo-labels to construct DSSL model for improving the reliability of the generated pseudo-labels. In this way, a Variational Information Domain Adaption (VIDA) module is constructed to solve the domain shift problems existed in the unstable working conditions. Moreover, the overall model is trained by Mini-Batch Incremental Learning (MBIL) strategy to build better underlying representation for supporting effective label propagation.
Underwater Image Enhancement Based on Adaptive Color Correction and Improved Retinex Algorithm

In order to solve the problems about color distortion and low contrast of underwater images, we propose an underwater image enhancement algorithm that combines adaptive color correction with improved Retinex algorithm. Our algorithm is a single-image enhancement method that does not require specialized hardware and underwater scenes prior. Firstly, the adaptive color correction is carried out on the underwater distorted images to solve the color cast problem effectively. Then, on the one hand, we use the image decomposition to strengthen the detail part and obtain a detail enhanced image. On the other hand, we use the improved Retinex algorithm to strengthen the edge part and obtain an edge enhanced image. Finally, the detail enhanced image and the edge enhanced image are fused based on the non-subsampled shearlet transform (NSST) to obtain the final enhanced underwater image. The results show that our method outperforms several state-of-the-art methods about underwater image enhancement in terms of PCQI, UCIQE, UIQM and IE. By scale invariant feature transform (SIFT) algorithm, we calculate the number of feature matching points of the input image and the enhanced image, and our proposed method achieves the best experimental results. The source code of our proposed algorithm is available at: https://github.com/lin9393/ underwater-image-enhance.
Graduates’ employment as an aspect of the university’s activities

The employment of graduates is an important direction in the work of the university management, which is caused by the need to preserve the theoretical knowledge, practical skills and immersion in the professional content of the graduate that were obtained during the period of his or her study and internship. This is possible in conditions of constant cooperation between the employer and the institution of higher education. The subject of the study is the activities of Peoples’ Friendship University of Russia, aimed at assisting in the employment of students, including the introduction of new work formats. The research method was a case study, the analysis of the results of which made it possible to present some recommendations that, in our opinion, will help orient universities at the very beginning of organizational work in this direction. The relevance of theoretical understanding of the practical experience of RUDN University is confirmed by the presence of scientific papers on the peculiarities of the employment of university graduates and the role of universities in this social phenomenon. Employment activities include such aspects as the creation and development of career centers at universities, informing and career counseling of students in such centers; promotion of students and graduates’ employment; employment monitoring; cooperation with partner companies. The study showed that the most important stages of successful activity are the creation of career centers in a particular university, the selection of target groups of students (Russian, international, disabled, handicapped), the appeal to modern forms of employment events (digital career environment, new online recruiting platforms; chatbot in social networks, online interview).
Comparative Study of Mfcc and Mel Spectrogram for Raga Classification Using CNN

Objectives: To perform a comparative study of the results of feature extraction done using two different methods, Mfcc and Mel spectrogram, and determine which method is more effective for implementing the CNN algorithm.Methods: This study uses the CNN model to classify ragas according to Indian classical music.Feature extraction, which is a major operation in the Music Information Retrieval (MIR) process, is done using Mfcc and Mel spectrogram methods.The major ragas chosen as subjects for feature extraction are Yaman, Bhairav, Bhairavi, Multani, and Dhanashree.Findings: After comparison and examination of results achieved from both techniques, we could conclude that the CNN model using the Mel spectrogram method outperforms the CNN model using Mfcc.Novelty: The majority of the research, we discovered was on Carnatic music.In contrast to earlier research, this research takes a novel approach by conducting experiments on a variety of Hindustani classical ragas which are different from other studies.Researchers interested in music as well as application users would benefit from this study.Our proposed feature extraction approach will be useful for initializing the CNN algorithm, which will help aspiring musicians recognize ragas and classify songs based on these ragas.
3D Object Watermarking from Data Hiding in the Homomorphic Encrypted Domain

For over a decade, 3D objects are an increasingly popular form of media. It has become necessary and urgent to secure them during their transmission or archiving. In this article, we propose a new method to obtain a watermarked 3D object from high-capacity data hiding in the encrypted domain. Based on the homomorphic properties of the Paillier cryptosystem, our proposed method allows us to embed several secret messages in the encrypted domain with a high-capacity. These messages can be extracted in the plain-text domain after the 3D object decryption. To the best of our knowledge, we are the first to propose a data hiding method in the encrypted domain where the high-capacity watermark is conserved in the plain-text domain after the 3D object is decrypted. The encryption and the data hiding in the encrypted domain are format compliant and without size expansion, despite the use of the Paillier cryptosystem. Each time a new message is embedded in the encrypted domain, flags are added in order to indicate which blocks are still available for the embedding of additional messages. After the decryption of a watermarked encrypted 3D object, our method produces a watermarked 3D object which is visually very similar to the original 3D object. From the decrypted watermarked 3D object, we can then extract all the embedded messages directly in the plain-text domain, without the need for an auxiliary file. Moreover, large keys are used, rending our method secure for real-life applications.
Non-contact photoplethysmographic sensors for monitoring students' cardiovascular system functional state in an IoT system

This article explores the technical feasibility of a hardware complex that employs photoplethysmographic sensors to measure the parameters of students' cardiovascular system functional state. The method of photoplethysmography utilizes non-contact sensors, which eliminate circulatory disorders caused by artery compression and enable the calculation of oxygen saturation via the pulse wave. The proposed hardware consists of several optocouplers arranged in series, parallel, or parallel-series configurations, with the mode of operation controlled by the intensity of the received pulse wave signal, depending on the individual's body constitution. The edge device hardware is a component of an Internet of Things (IoT) system that includes another edge device, which instantaneously transmits data to a database on the edge server for further processing and storage.