Reviewerly/exHarmony huggingface dataset:
Sparrow Search Optimization with Transfer Learning-Based Crowd Density Classification

Due to the rapid increase in urbanization and population, crowd gatherings are frequently observed in the form of concerts, political, and religious meetings.HAJJ is one of the well-known crowding events that takes place every year in Makkah, Saudi Arabia.Crowd density estimation and crowd monitoring are significant research areas in Artificial Intelligence (AI) applications.The current research study develops a new Sparrow Search Optimization with Deep Transfer Learning based Crowd Density Detection and Classification (SSODTL-CD2C) model.The presented SSODTL-CD2C technique majorly focuses on the identification and classification of crowd densities.To attain this, SSODTL-CD2C technique exploits Oppositional Salp Swarm Optimization Algorithm (OSSA) with EfficientNet model to derive the feature vectors.At the same time, Stacked Sparse Auto Encoder (SSAE) model is utilized for the classification of crowd densities.Finally, SSO algorithm is employed for optimal fine-tuning of the parameters involved in SSAE mechanism.The performance of the proposed SSODTL-CD2C technique was validated using a dataset with four different kinds of crowd densities.The obtained results demonstrated that the proposed SSODTL-CD2C methodology accomplished an excellent crowd classification performance with a maximum accuracy of 93.25%.So, the proposed method will be highly helpful in managing HAJJ and other crowded events.
Depth-Adaptive Deep Neural Network Based on Learning Layer Relevance Weights

In this paper, we propose two novel Adaptive Neural Network Approaches (ANNAs), which are intended to automatically learn the optimal network depth. In particular, the proposed class-independent and class-dependent ANNAs address two main challenges faced by typical deep learning paradigms. Namely, they overcome the problems of setting the optimal network depth and improving the model interpretability. Specifically, ANNA approaches simultaneously train the network model, learn the network depth in an unsupervised manner, and assign fuzzy relevance weights to each network layer to better decipher the model behavior. In addition, two novel cost functions were designed in order to optimize the layer fuzzy relevance weights along with the model hyper-parameters. The proposed ANNA approaches were assessed using standard benchmarking datasets and performance measures. The experiments proved their effectiveness compared to typical deep learning approaches, which rely on empirical tuning and scaling of the network depth. Moreover, the experimental findings demonstrated the ability of the proposed class-independent and class-dependent ANNAs to decrease the network complexity and build lightweight models for less overfitting risk and better generalization.
Sailfish Optimizer with Deep Transfer Learning-Enabled Arabic Handwriting Character Recognition

The recognition of the Arabic characters is a crucial task in computer vision and Natural Language Processing fields.Some major complications in recognizing handwritten texts include distortion and pattern variabilities.So, the feature extraction process is a significant task in NLP models.If the features are automatically selected, it might result in the unavailability of adequate data for accurately forecasting the character classes.But, many features usually create difficulties due to high dimensionality issues.Against this background, the current study develops a Sailfish Optimizer with Deep Transfer Learning-Enabled Arabic Handwriting Character Recognition (SFODTL-AHCR) model.The projected SFODTL-AHCR model primarily focuses on identifying the handwritten Arabic characters in the input image.The proposed SFODTL-AHCR model pre-processes the input image by following the Histogram Equalization approach to attain this objective.The Inception with ResNet-v2 model examines the pre-processed image to produce the feature vectors.The Deep Wavelet Neural Network (DWNN) model is utilized to recognize the handwritten Arabic characters.At last, the SFO algorithm is utilized for fine-tuning the parameters involved in the DWNN model to attain better performance.The performance of the proposed SFODTL-AHCR model was validated using a series of images.Extensive comparative analyses were conducted.The proposed method achieved a 5468 CMC, 2023, vol.74, no.3 maximum accuracy of 99.73%.The outcomes inferred the supremacy of the proposed SFODTL-AHCR model over other approaches.
Automated Arabic Text Classification Using Hyperparameter Tuned Hybrid Deep Learning Model

The text classification process has been extensively investigated in various languages, especially English.Text classification models are vital in several Natural Language Processing (NLP) applications.The Arabic language has a lot of significance.For instance, it is the fourth mostly-used language on the internet and the sixth official language of the United Nations.However, there are few studies on the text classification process in Arabic.A few text classification studies have been published earlier in the Arabic language.In general, researchers face two challenges in the Arabic text classification process: low accuracy and high dimensionality of the features.In this study, an Automated Arabic Text Classification using Hyperparameter Tuned Hybrid Deep Learning (AATC-HTHDL) model is proposed.The major goal of the proposed AATC-HTHDL method is to identify different class labels for the Arabic text.The first step in the proposed model is to pre-process the input data to transform it into a useful format.The Term Frequency-Inverse Document Frequency (TF-IDF) model is applied to extract the feature vectors.Next, the Convolutional Neural Network with Recurrent Neural Network (CRNN) model is utilized to classify the Arabic text.In the final stage, the Crow Search Algorithm (CSA) is applied to fine-tune the CRNN model's hyperparameters, showing the work's novelty.The proposed AATC-HTHDL model was experimentally validated under different parameters and 5448 CMC, 2023, vol.74, no.3 the outcomes established the supremacy of the proposed AATC-HTHDL model over other approaches.
Leveraging Transfer Learning for Spatio-Temporal Human Activity Recognition from Video Sequences

Human Activity Recognition (HAR) is an active research area due to its applications in pervasive computing, human-computer interaction, artificial intelligence, health care, and social sciences.Moreover, dynamic environments and anthropometric differences between individuals make it harder to recognize actions.This study focused on human activity in video sequences acquired with an RGB camera because of its vast range of real-world applications.It uses two-stream ConvNet to extract spatial and temporal information and proposes a fine-tuned deep neural network.Moreover, the transfer learning paradigm is adopted to extract varied and fixed frames while reusing object identification information.Six state-of-the-art pre-trained models are exploited to find the best model for spatial feature extraction.For temporal sequence, this study uses dense optical flow following the two-stream Con-vNet and Bidirectional Long Short Term Memory (BiLSTM) to capture longterm dependencies.Two state-of-the-art datasets, UCF101 and HMDB51, are used for evaluation purposes.In addition, seven state-of-the-art optimizers are used to fine-tune the proposed network parameters.Furthermore, this study utilizes an ensemble mechanism to aggregate spatial-temporal features using a four-stream Convolutional Neural Network (CNN), where two streams use RGB data.In contrast, the other uses optical flow images.Finally, the proposed ensemble approach using max hard voting outperforms state-ofthe-art methods with 96.30% and 90.07%accuracies on the UCF101 and HMDB51 datasets.
A Processor Performance Prediction Method Based on Interpretable Hierarchical Belief Rule Base and Sensitivity Analysis

The prediction of processor performance has important reference significance for future processors.Both the accuracy and rationality of the prediction results are required.The hierarchical belief rule base (HBRB) can initially provide a solution to low prediction accuracy.However, the interpretability of the model and the traceability of the results still warrant further investigation.Therefore, a processor performance prediction method based on interpretable hierarchical belief rule base (HBRB-I) and global sensitivity analysis (GSA) is proposed.The method can yield more reliable prediction results.Evidence reasoning (ER) is firstly used to evaluate the historical data of the processor, followed by a performance prediction model with interpretability constraints that is constructed based on HBRB-I.Then, the whale optimization algorithm (WOA) is used to optimize the parameters.Furthermore, to test the interpretability of the performance prediction process, GSA is used to analyze the relationship between the input and the predicted output indicators.Finally, based on the UCI database processor dataset, the effectiveness and superiority of the method are verified.According to our experiments, our prediction method generates more reliable and accurate estimations than traditional models.
A Coprocessor Architecture for 80/112-bit Security Related Applications

We have proposed a flexible coprocessor key-authentication architecture for 80/112-bit security-related applications over GF (2 m ) field by employing Elliptic-curve Diffie Hellman (ECDH) protocol.Towards flexibility, a serial input/output interface is used to load/produce secret, public, and shared keys sequentially.Moreover, to reduce the hardware resources and to achieve a reasonable time for cryptographic computations, we have proposed a finite field digit-serial multiplier architecture using combined shift and accumulate techniques.Furthermore, two finite-statemachine controllers are used to perform efficient control functionalities.The proposed coprocessor architecture over GF 2 163 and GF 2 233 is programmed using Verilog and then implemented on Xilinx Virtex-7 FPGA (field-programmable-gate-array) device.For GF 2 163 and GF 2 233 , the proposed flexible coprocessor use 1351 and 1789 slices, the achieved clock frequency is 250 and 235 MHz, time for one public key computation is 40.50 and 79.20 μs and time for one shared key generation is 81.00 and 158.40 μs.Similarly, the consumed power over GF 2 163 and GF 2 233 is 0.91 and 1.37 mW , respectively.The proposed coprocessor architecture outperforms state-of-the-art ECDH designs in terms of hardware resources.
Encrypted Storage Method of Oral English Teaching Resources based on Cloud Platform

With the development of the times, the secure storage of educational resources has become one of the key security problems faced by colleges and universities.On the one hand, the cost of traditional resource storage is too expensive, on the other hand, its encryption and access efficiency are low.To solve this problem, this research takes the cloud platform serves as the main carrier for the encrypted storage of school teaching resources.On this basis, the convolutional neural network is encrypted and optimized, and the argmax algorithm is improved to improve the access efficiency of encrypted data.Finally, the effectiveness and superiority of the design method are compared and analyzed through the method of performance detection.The results show that the maximum consumption time of encryption and decryption of the encrypted storage model is no more than 20000ms, which is significantly less than that of the traditional model.The running time of the argmax output encryption module is 1.76ms and the running loss is 0.26 MB, which is less than that of the traditional model.It can be seen that the encrypted storage model has stronger encryption performance and access performance, and has a better application effect in the encrypted storage of oral English teaching resources with a large amount of access data and frequent updates.
A Semantic NoSQL Application Program Interface for Big Data

Complexity, heterogeneity, schemaless-ness, data visualization, and extraction of consistent knowledge from Big Data are the biggest challenges in NoSQL databases.This paper presents a general semantic NoSQL Application Program Interface that integrates and converts NoSQL databases to semantic representation.The generated knowledge base is suitable for visualization and knowledge extraction from different Big Data sources.The authors use a case study of the COVID-19 pandemic prediction and other weather occurrences in various parts of the world to illustrate the suggested API.The Authors find a correlation between COVID-19 spread and deteriorating weather.According to the experimental findings, the API's performance is enough for heterogeneous Big Data.
Modeling and Simulation of a Blockchain Consensus for IoT Node Data Validation

The classical blockchain developed for the Bitcoin cryptocurrency has evolved since its introduction more than a decade ago.Blockchain exists in different forms for different purposes and operational contexts.There has been a significant growth in the business use cases of blockchain which is based on the unique attributes of the distributed ledger technology.Blockchain provides peer-to-peer distribution of data in a traceable and decentralized architecture that attains data authentication using consensus protocols.Blockchain as a distributed ledger is the fusion of cryptography, peer-to-peer networking technology, distributed system technology, and consensus mechanism to assure information security and digital asset management.Consensus mechanisms are applied to the distributed ledger that operates in a peer-to-peer network where message transmission between peers is validated and stored across all active peers.Reaching an agreement to validate message transmission and maintaining the correctness of the state of data in a network for critical wireless sensor networks have become a necessary requirement for networks that span several subsystems covering a large operational area.Due to the resource constrained nature of the active actors of wireless sensor networks, any cryptographic solution to be adopted must be lightweight and efficient as well.This paper proposes a blockchain-based decentralized mechanism for authentication of node data for storage onto a distributed ledger.The coloured Petri net was used to model and simulate by detailing the critical attributes of the workings of the system that is based on cyberphysical IoT architecture.
Libraries of Orthodox Theological Schools of the Russian Empire

The article is devoted to the reading circle of students of theological schools of the 19th -early 20th centuries.In the Russian Empire, libraries played an exceptional role in religious education, being part of the system of state disciplinary practices aimed at building a "new world".The goal is to analyze the infosphere of Orthodox theological schools, which implies research of the reading circle of both teachers and students based on a study of the book collections of theological seminaries and academies, personal book collections of teachers and bishops, and periodical literature.Archival documents and memoirs are also valuable sources.The composition of libraries, as a component of the infosphere of the Orthodox religious institutions of the Russian Empire in the 19th -early 20th centuries, seems to be a clear catalyst in the analysis of Russian cultural and historical discourse and the history of religious, philosophical, and theological thought.The formation and replenishment of the collections of the libraries of theological schools, seminaries and academies reflects the history of these educational institutions themselves.The student's and teacher's circle of reading was not limited only by various kinds of philosophical and theological literature, but secular fiction and periodicals were also popular.The composition of the collections, especially student's libraries, reflected the socio-cultural dynamics within the Russian Empire.The fundamental reason for this proposal is that the infosphere of Orthodox theological schools of the Russian Empire in the 19th-early 20th century deserves a comprehensive analysis, especially relevant in the time of active growth of theological education in modernday Russia.
Attitudes about Cybersecurity Articulation Agreements and Transfer Students: A Statewide Survey of Faculty Members and Advisors

In this study, cybersecurity faculty and academic advisors from community colleges and 4-year universities in the southeast region of the United States completed a survey assessing attitudes about and support for articulation agreements and related transfer policies. Hypothesizing that professional structures shape attitudes and experiences, the researchers conducted an exploratory quantitative study with primarily descriptive analyses. The results reveal differences in attitudes between community college and 4-year stakeholders and between faculty and academic advisors. The results of this study are discussed in relation to faculty and advisor training and communication.
Evaluating the Social Media Profiles of Online Harassers

Online harassment is pervasive. While substantial research has examined the nature of online harassment and how to moderate it, little work has explored how social media users evaluate the profiles of online harassers. This is important for helping people who may be experiencing or observing harassment to quickly and efficiently evaluate the user doing the harassing. We conducted a lab experiment (N=45) that eye-tracked participants while they viewed profiles of users who engaged in online harassment on mock Facebook, Twitter, and Instagram profiles. We evaluated what profile elements they looked at and for how long relative to a control group, and their qualitative attitudes about harasser profiles. Results showed that participants look at harassing users' post history more quickly than non-harassing users. They are also somewhat more likely to recall harassing profiles than non-harassing profiles. However, they do not spend more time on harassing profiles. Understanding what users pay attention to and recall may offer new design opportunities for supporting people who experience or observe harassment online.
Exploring the Effect of High-frequency Components in GANs Training

Generative Adversarial Networks (GANs) have the ability to generate images that are visually indistinguishable from real images. However, recent studies have revealed that generated and real images share significant differences in the frequency domain. In this article, we argue that the frequency gap is caused by the high-frequency sensitivity of the discriminator. According to our observation, during the training of most GANs, severe high-frequency differences make the discriminator focus on high-frequency components excessively, which hinders the generator from fitting the low-frequency components that are important for learning images’ content. Then, we propose two simple yet effective image pre-processing operations in the frequency domain for eliminating the side effects caused by high-frequency differences in GANs training: High-frequency Confusion (HFC) and High-frequency Filter (HFF). The proposed operations are general and can be applied to most existing GANs at a fraction of the cost. The advanced performance of the proposed operations is verified on multiple loss functions, network architectures, and datasets. Specifically, the proposed HFF achieves significant improvements of 42.5% FID on CelebA (128*128) unconditional generation based on SNGAN, 30.2% FID on CelebA unconditional generation based on SSGAN, and 69.3% FID on CelebA unconditional generation based on InfoMAXGAN. Furthermore, we also adopt HFF as the first attempt at data augmentation in the frequency domain for contrastive learning, achieving state-of-the-art performance on unconditional generation. Code is available at https://github.com/iceli1007/HFC-and-HFF .
Improving Non-Native Speakers' Participation with an Automatic Agent in Multilingual Groups

Non-native speakers (NNS) often face challenges gaining the speaking floor in conversations with native speakers (NS) of a common language. To help NNS to contribute more, we developed a conversational agent that opens up the speaking floor either automatically, after NS have taken a certain number of consecutive speaking turns, or manually, upon NNS request. We compared these automatic and manual agents to a control condition in a laboratory study in which one NNS collaborated with two NS using English as a common language. Participants (N=48) communicated over video conferencing from separate locations in a research institution to collaborate on three survival tasks. Based on data gathered from the experiments, the automatic agent encouraged NNS to participate more, which previous studies had attempted but failed to achieve. Excerpts from group discussions further showed the crucial role of the automatic agent on NNS participation. Interview results suggested that while NNS appreciated the automatic agent's help to participation, NS perceived the agent's interruption as unfair because they thought all members were speaking equally, which was not the case. The mismatch in their perceptions further emphasizes the need to intervene, and we provide design implications based on the results.
Analytical expression and simulation study of quantum radar scattering characteristics of triangular pyramid

Due to the constant development of quantum radar technology, it has become increasingly important to study the scattering properties of quantum radars of typical targets. In this study, the analytical expression of the triangular pyramid is determined by solving the Fourier transform of each plane of the triangular pyramid, and their quantum radar scattering cross-section, to propose a solution to the equivalent transformation of the elevation angle and azimuth angle of the inclined plane. Besides, based on the simulation result of the triangular pyramid QRCS, it can be seen that the results obtained by using this calculation are consistent with the numerical solutions, and that the calculation time is significantly reduced.
Rational wavelet filter banks from Blaschke product

This note designs two kinds of rational wavelet filter banks using three basic bricks: the finite Blaschke product, Bezout polynomial and the symbol of the cardinal B-spline. In orthogonal case, the corresponding wavelets are generalization of Daubechies’ wavelets. The role of the Blaschke product is the adjustment of the peaks of wavelet functions.
Improved secure dynamic bit standard technique for a private cloud platform to address security challenges

The numerous technologies and procedures used to safeguard private cloud infrastructure are private cloud security. Despite multitenant public cloud environments, resources are dedicated to individual companies in a private cloud. Cloud computing uses several data security mechanisms, including authentication and identification, access control, encryption, secure deletion, integrity checking, and data masking. Cloud computing relies heavily on security and traffic management. Because data must be transferred from a local computer to a remote computer when using a cloud computing service, data protection is becoming an increasingly important security problem. Research on cloud computing-based secure dynamic bit standard (CC-SDBS) technology has increased data security. This technique’s cryptographic algorithms (CA) improve cloud computing security. As a result of the private cloud security level algorithm (PCSLA), the client’s private data can be easily accessed in the cloud. For the client’s private cloud data, PCSLA provides dynamic security measures. Cloud computing applications can use the tests’ better data security in the future. The proposed algorithm’s testing results have shown a noticeable improvement in cipher size and execution time compared with other commonly used cloud computing. Numerical results revealed that CC-SDBS improved average energy consumption by 20%, average network latency by 25%, average key generation time analysis by 4.3%, and average network use by 15%.