Reviewerly/exHarmony huggingface dataset:
Blockchain- Based Secure and Efficient Scheme for Medical Data

Internet of Things (IoT) fog nodes are distributed near end-user devices to mitigate the impacts of low delay, position awareness, and spatial spread, which aren't permitted by numerous IoT apps. Fog computing (FC) also speeds up reaction times by decreasing the quantity of data sent to the cloud. Despite these advantages, FC still has a lot of work to do to fulfill security and privacy standards. The constraints of the FC resources are the cause of these difficulties. In reality, FC could raise fresh concerns about privacy and security. Although the Fog security and privacy problems have been covered in several articles recently, most of these studies just touched the surface of these difficulties. This paper provides a unique solution for the authentication of data by using hyperledger fabric. The fog layer store data transferred by the IoT layer and calculate the hash value. These hash values are now stored in hyperledger fabric for authentication purposes. The proposed model results compared with lewako’s and Fan’s scheme and found that the proposed model has 25.00 % less encryption time, 09.3 % less decryption time, 17.48 % less storage overhead, and 23.38 % less computation cost as compared to Fan’s scheme.
Toward reliable fusion object detection based on dilated pyramid and semantic attention

Abstract Object detection on fused images of visible and infrared modals is of great importance for many applications, for example, surveillance and rescue at low‐light conditions. However, current detectors have difficulty for robust fused image detection for mainly two reasons. First, objects are presented in various shapes and sizes, making some hard samples cannot be localized accurately. Second, the same object category in the fused images will have different appearance due to changing weather condition, temperature and intrinsic heat. Such a contradiction will degrade the classification task of a detection network, since it cannot merge commonalities and distinguish differences well. In this paper, we propose to reconstruct the detection pipeline of current detectors, and enhance the detection ability on difficult samples in fused images. Specifically, a Dilation Pyramid Network (DPN) is designed at the lateral connection to generate and aggregate features of various receptive field, without increasing pyramid layers. To strengthen the classification, a Semantic Category Attention Module (SCAM) is proposed to capture attention centers of semantics in fused images, rather than object centers. Abundant experiments on two fusion datasets show that the proposed method achieves a satisfying performance, and both modules can greatly improve current generic detectors on fused images.
PYCASO: Python module for calibration of cameras by Soloff’s method

The PYthon module for the CAlibration of cameras by SOloff’s method (PYCASO) provides an open-source Python-based framework for stereoscopic reconstructions from pairs of 2D images. The determination of a matching function between image and physical coordinates is essential to accurately build 3D representations at any scales. Moreover, at the finest scales, this relationship is strongly non-linear, mainly due to optical distortions of the cameras (such as misalignment of the axis of the lens with that of the objective, refraction of optical windows etc.). Therefore, this paper proposes a general and accurate method based on the Soloff method through the PYCASO module. While the basic method is time-consuming, an acceleration of the procedure is proposed by relying on artificial intelligence (AI).
THE RELATIONSHIP OF LEADERSHIP POTENTIAL OF FUTURE SPECIALISTS FROM LAW ENFORCEMENT ACTIVITIES OF THE PENITENTIARY SYSTEM OF UKRAINE WITH THEIR EDUCATIONAL MOTIVATION AND THE LEADERSHIP STYLE OF TEACHERS

Abstract: it is determined in the article that teachers’ leadership behavior has a significant relation with the perception of their leadership potential by future specialists in law enforcement in the penitentiary system of Ukraine. For ensuring successful formation of leadership potential in future specialists in law enforcement in the penitentiary system of Ukraine, the following can be recommended to pedagogues at training institutions: first, to develop and improve their own leadership style; second, to efficiently use the motivation of applicants for education so that they could discover their leadership potential.
An Efficient Pairing-Free Certificateless Signature Scheme with KGC Trust Level 3 for Wireless Sensor Network

With the widespread adoption of wireless sensor networks (WSN), the security of the WSN has been a wide concern. Certificateless signature eliminates the certificate management problem and key escrow problem and is considered a feasible solution to solve the data integrity and authentication of WSN. Recently, Thumbur et al. proposed an efficient pairing-free certificateless signature scheme, and Xu et al. pointed out that their scheme is not resistant to signature forgery attacks and proposed an improved scheme. Based on the trust hierarchy defined by Girault, we find that Xu et al.’s scheme is still only able to achieve security under KGC trust level 2. Moreover, Thumbur et al.’s scheme uses the Schnorr signature algorithm form, which makes it favorable for scaling, while Xu et al.’s scheme breaks this advantage. Therefore, we propose a pairing-free certificateless scheme capable of reaching KGC trust level 3, still using the Schnorr signature algorithm form, and prove the security of the new scheme under the random oracle model. The final efficiency analysis shows that the new scheme has shorter public key length and higher computational efficiency.
A Method for Improving the Performance of Ensemble Neural Networks by Introducing Randomization into Their Training Data

We propose a methodology for training neural networks in which ensembles of under-trained neural networks are used to obtain broadly repeatable predictions, and we augment their performance by disrupting their training, with each neural network in the ensemble being trained on a potentially different data set generated from the base data by a method that we call randomization with full range sampling. Sleep habits in animals are a function of innate and environmental factors that determine the species’ place in the ecosystem and, thus, its requirement for sleep and opportunity to sleep. We apply the proposed methodology to train neural networks to predict hours of sleep from only seven correlated observations in only 39 species (one set of observations per species). The result was an ensemble of neural networks making more accurate predictions (lower mean squared error) and predictions that are more robust against variations in any one input parameter. The methodology presented here can be extended to other problems in which the data available for training are limited, or the neural network is to be applied, post-training, on a problem with substantial variation in the values of inputs (independent variables).
Improving Quality of Watermarked Medical Images Using Symmetric Dilated Convolution Neural Networks

Rapid development of online medical technologies raises questions about the security of the patient’s medical data.When patient records are encrypted and labeled with a watermark, they may be exchanged securely online. In order to avoid geometrical attacks aiming to steal the information, image quality must be maintained and patient data must be appropriately extracted from the encoded image. To ensure that watermarked images are more resistant to attacks (e.g. additive noise or geometric attacks), different watermarking methods have been invented in the past. Additive noise causes visual distortion and render the potentially harmful diseases more difficult to diagnose and analyze. Consequently, denoising is an important pre-processing method for obtaining superior outcomes in terms of clarity and noise reduction and allows to improve the quality of damaged medical images. Therefore, various publications have been studied to understand the denoising methods used to improve image quality. The ﬁndings indicate that deep learning and neural networks have recently contributed considerably to the advancement of image processing techniques. Consequently, a system has been created that makes use of machine learning to enhance the quality of damaged images and to facilitate the process of identifying specific diseases. Images, damaged in the course of an assault, are denoised using the suggested technique relying on a symmetric dilated convolution neural network. This improves the system’s resilience and establishes a secure environment for the exchange of data while maintaining secrecy.
Multi-modal recommendation algorithm fusing visual and textual features

In recommender systems, the lack of interaction data between users and items tends to lead to the problem of data sparsity and cold starts. Recently, the interest modeling frameworks incorporating multi-modal features are widely used in recommendation algorithms. These algorithms use image features and text features to extend the available information, which alleviate the data sparsity problem effectively, but they also have some limitations. On the one hand, multi-modal features of user interaction sequences are not considered in the interest modeling process. On the other hand, the aggregation of multi-modal features often employs simple aggregators, such as sums and concatenation, which do not distinguish the importance of different feature interactions. In this paper, to tackle this, we propose the FVTF (Fusing Visual and Textual Features) algorithm. First, we design a user history visual preference extraction module based on the Query-Key-Value attention to model users' historical interests by using of visual features. Second, we design a feature fusion and interaction module based on the multi-head bit-wise attention to adaptively mine important feature combinations and update the higher-order attention fusion representation of features. We conduct experiments on the Movielens-1M dataset, and the experiments show that FVTF achieved the best performance compared with the benchmark recommendation algorithms.
Comparative analysis of selected programming frameworks dedicated to SPA applications

The purpose of this study was to compare two popular programming frameworks dedicated to building SPA applications. Two proprietary applications with identical functionalities were analyzed, one created in React and the other in Angular. A ready-made programming tool was used, an original script comparing the speed of adding elements to the database, and the popularity of the technology was checked on the Stack Overflow website and in the Google search engine.
A Management Specification for Data Sharing Security in the System Construction of Smart Mine

With the development of Internet of Things technology and the informatization of the coal industry, various intelligent applications have emerged in the process of the system construction of the smart mine. During this process, data sharing is essential to the effective use of data resources in the smart mine. In order to improve the protection of coal mine data, this study proposes a set of management specifications for data sharing applied to the system construction of a smart mine to unify security standards in data storage and sharing. It standardizes the processes of data collection, transmission, and storage. We design three subspecifications for these processes, namely, data source specification, data quality specification, and data storage specification. The data source specification specifies the data collection and transmission standards to improve the security and timeliness of data sharing. The data quality specification sets three evaluation criteria of integrity, accuracy, and timeliness according to the characteristics of each business system and data. The system ensures data quality during data sharing by governing and recording the data failing to meet the criteria. The data storage specification specifies the data storage protocol, data label, and data set restrictions. Only authorized platforms and users can share data and make use of data labels to search data efficiently. Finally, we constructed a coal mine data collection and analysis system. It can collect, manage, store, and safely share the real measured data from a certain colliery according to the specifications.
A Phenomenological Study of The Implementation of Indonesian Language Learning During the Covid-19 Pandemic

This research was motivated by a number of phenomena that occurred in the implementation of online learning at SMP Negeri 3 Pelalawan, Pelalawan Regency. This study uses a qualitative approach with phenomenological methods. Based on the results of the analysis and discussion, it is concluded that the obstacles that occur in the implementation of learning Indonesian are as follows: first, the network is less stable; second, the learning atmosphere is not comfortable because many friends are noisy because the network is not good; third, learning videos are intermittent; fourth, in providing material via whatsapp so that it does not include learning objectives, students do not understand the previous material; fifth, students are late to enter the zoom application because they have to find a network to the forest or the pier. In addition, there are students who do not have cellphones, so they have to wait for their parents to come home from work before they can communicate with the teacher; and Sixth, the internet quota that runs out during the closing learning implementation, if the electricity goes out, the internet network will disappear.
Quantum encryption in phase space with displacement operators

Abstract In photonic computing, the quantum systems consist of coherent states and squeezed coherent states. Common quantum gates found in these systems are: phase shift, displacement, and squeezing gates. These gates are all unitary and reversible. Outside of quantum systems, coherent states also plays a significant role in coherent optical communications with speeds of hundreds of gigabits per second. Secure optical communications is generally implemented at the data layer with classical symmetric encryption such as Advanced Standard Encryption or AES. This inevitably allows any wiretapping to capture the transmitted data either in the plaintext mode or in the encrypted ciphertext mode in the optical infrastructure. The recent and rapid developments in Quantum computing further lift up the need for quantum secure communications in the optical infrastructure. This paper proposes a novel quantum encryption in the coherent optical domain utilizing a displacement operator and implementing with IQ-MZM optical modules, called Quantum Encryption in Phase Space or QEPS. The communication peers share a secret used to seed cryptographic pseudo random number generators to produce a synchronized random number at both the transmitter and receiver. The synchronized random numbers are used to establish displacement operators to encrypt the coherent states at the transmission and decrypt the cipher coherent states at the receiver. Therefore, malicious parties tapping along the fibre line would not extract the message in transit from optical domain due to a high Bit Error Rate or BER. The optimal displacement operator is split into a standard 16-QAM and a random phase shift operator to enhance the transmission security. We analysis the transmission security with the wiretap channel model for semantic security. We have simulated the QEPS encryption and decryption for two data modulation schemes: QPSK and 16-QAM over 80 km for transmission speeds of 56 Gbps for QPSK and 112 Gbps for 16-QAM.
Facial expressions capturing emotional engagement while learning with desktop VR: the impact of emotional regulation and personality traits

Virtual reality (VR) has been shown to induce excessive affective processing, which in turn impacts the learning process and learning outcomes. Therefore, a better understanding of emotional dynamics and how emotions are instigated while learning with VR is needed. For this purpose, the students learning process with a VR simulation was captured using continuous measurement of facial expressions. In addition, personality traits and self-regulated emotion (SRE) characteristics were examined. The findings suggest that overall, joy was the most predominant emotion expressed by students, while intraindividual components of personality traits and SRE strategies were associated with negative emotional expressions. Specifically, students with high conscientiousness or agreeableness personality traits tended to experience less anger and/or sadness. In addition, the findings demonstrate that the experience of these negative emotions was mediated by the SRE strategies of reappraisal and suppression, implying a mechanism of emotional engagement while learning with VR. This suggests that while individual personality traits are relatively stable and immutable, SRE preferences can be supported as part of the VR instructional design and scaffolding. Future studies should explore how students’ emotion regulation preferences can be supported to benefit their emotional experiences while learning.
Characterization of Gold Bearing Placers and Associated Minerals in the Elogo Region (North-West Congo Republic)

The Elogo region has been subjected to craft industry and semi-industrial mining for gold-bearing placers, since the colonial times.It is actually undergoing an intensive exploration for a primary gold deposit.The goal of this study is to contribute to the exploration of the primary gold deposit in the Elogo Region (North-West of Congo Republic).The methodology consisted of characterization of placers deposits by their lithology, mineralogy and the gold grains shape, in order to constrain the source of gold with the respect to the local geology.The results obtained show that alluviums are polygenic and yields seventeen mineral species composed of zircon, olivine, magnetite, ilmenite, gold, garnet, rutile, coltan, cassiterite, monazite, apatite, amphibole, tourmaline, pyrite, limonite, chromite, and amphibole.The morphoscopy of gold grain shows single grains and grains with quartz inclusions that suggest their relationship with quartz veins.The gold grains are flattened, sub-flattened, and rounded.The northern region of Elogo characterized by a dominance of coarser quartz-included gold grains indicates a proximal proparte origin (less than 50 m), while the southern region, showing less coarse and more evolved grains with choc marks, appears to be associated with a distal pro-parte origin (more than 300 m).The multivariate statistical analysis shows seven classes of samples corresponding to the mineralogical paragenesis suggesting various sources, consisting of high-grade metamorphic, granitoids, pegmatites, basic and ultrabasic rocks, and BIFs that provided minerals to the placers.Gold mainly comes from hydrothermal quartz veins and proba-
A domain translation network with contrastive constraint for unpaired motion image deblurring

Abstract Most motion deblurring methods require a large amount of paired training data, which is nearly unreachable in practice. To overcome the limitation, a domain translation network with contrastive constraint for unpaired motion image deblurring is proposed. First, a domain translation network with two streams, a sharp domain translation stream and a blurred domain translation stream, to handle unpaired sharp and blurred images from the real world is presented. Second, a contrastive constraint loss in the deep intermediate level for the two streams to promote the network to produce deblurring results close to the real sharp image is proposed. Third, distinct loss functions for the two streams to preserve the edge and texture detail of the deblurring image is designed. Extensive experiments on several benchmark datasets demonstrate that the proposed network achieves better visual performance than the current state‐of‐the‐art methods for unpaired motion image deblurring.
Asessing the Role of Internet Development on Entrepreneurial Activity

This study investigated the impact of internet development, including internet usage, fixed broadband subscriptions, and secure internet servers, on entrepreneurial activity, proxied by Global Entrepreneurship Index (GEI).This study, which covered 109 countries from 2015 to 2019, used the fixed panel regression method.The result showed that internet usage and secure internet servers have significant negative effects on the GEI.In the case of the economic classifications, the internet variables have a significant negative effect on the global entrepreneurship index in emerging and developing economies.However, in the case of advanced economies, internet variables have no significant effect on GEI.The effect of digitalization through internet technology must be considered for policymakers to manage the entrepreneurial ecosystems.The integration between entrepreneurship and internet could maintain the higher technological development to increase the higher output.
Investigation of the influence of blocks on the linear spectrum for synthetic speech detection

Abstract In recent years, constant‐Q cepstral coefficient (CQCC) has successfully used in synthetic speech detection (SSD), however, linear‐domain logarithm power spectrum (LLPS) information is not fully captured by discrete cosine transform (DCT) in CQCC extraction and how the block affects the LLPS information extraction for SSD has not been investigated in the previous studies. In order to investigate how the block affects the LLPS information extraction and extract more detailed information from LLPS for SSD, a new feature, constant‐Q block coefficient (CQBC), is proposed by modifying CQCC using block transform plus DCT on LLPS in this letter. Furthermore, how the length of block affects the performance is deeply investigated in this work. The experimental result on ASVspoof 2015 evaluation set indicates that: (1) there is much difference in the performance for different length block (2) CQBC‐DA can improve the performance of SSD and its average equal error rate can reach 0.0724, which decline 36% compared with CQCC‐DA (3) CQBC‐DA is superior to many front‐ends that have been benchmarked in terms of average equal error rate.
Emotional Intelligence Differences Between Male and Female Adolescents

This study aims to determine differences in the level of emotional intelligence in male and female adolescents. This research is a type of comparative descriptive quantitative research. The population used in the study was 1060 students of SMAN 1 Kampar Timur who were enrolled in the 2022/2023 school year with a sample of 290 students who were selected using the proportional random sampling technique. Data collection was carried out using an emotional intelligence questionnaire which was arranged based on aspects of emotional intelligence. Data were processed using descriptive analysis techniques and comparative analysis techniques T-Test. The results showed that: 1) as many as 38.6% of students have moderate emotional intelligence; 2) as many as 37.2% of male adolescents have moderate emotional intelligence of male adolescents; 3) as many as 35.9% of young women have moderate emotional intelligence; 4) there is no difference in emotional intelligence in middle adolescents in terms of gender with the result sig = 0.078 (sig. &gt; 0.05).TRANSLATE with x EnglishArabicHebrewPolishBulgarianHindiPortugueseCatalanHmong DawRomanianChinese SimplifiedHungarianRussianChinese TraditionalIndonesianSlovakCzechItalianSlovenianDanishJapaneseSpanishDutchKlingonSwedishEnglishKoreanThaiEstonianLatvianTurkishFinnishLithuanianUkrainianFrenchMalayUrduGermanMalteseVietnameseGreekNorwegianWelshHaitian CreolePersian // TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster PortalBack//