Reviewerly/exHarmony huggingface dataset:
TeFNA: Text-centered fusion network with crossmodal attention for multimodal sentiment analysis

Multimodal sentiment analysis (MSA), which goes beyond the analysis of texts to include other modalities such as audio and visual data, has attracted a significant amount of attention. An effective fusion of sentiment information in multiple modalities is key to improving the performance of MSA. However, aligning multiple modalities during the process of fusion faces challenges such as maintaining modal-specific information. This paper proposes a Text-centered Fusion Network with crossmodal Attention (TeFNA), a multimodal fusion network that uses crossmodal attention to model unaligned multimodal timing information. In particular, TeFNA employs a Text-Centered Aligned fusion method (TCA) that takes text modality as the primary modality to improve the representation of fusion features. In addition, TeFNA maximizes the mutual information between modality pairs to maintain task-related emotional information, thereby ensuring that the key information of modalities from input to fusion is preserved. The results of our comprehensive experiments on the multimodal datasets of CMU-MOSI and CMU-MOSEI show that our proposed model outperforms methods in terms of most metrics used.
The ISO/IEC 27001 Information Security Management Standard: How to Extract Value from Data in the IT Sector

In order to handle their regulatory and legal responsibilities and to retain trustworthy strategic partnerships, enterprises need to be dedicated to guaranteeing the privacy, accessibility, and authenticity of the data at their disposal. Companies can become more resilient in the face of information security threats and cyberattacks by effectively integrating security strategies. The goal of this article is to describe a plan that a corporation has implemented in the information technology industry in order to ensure compliance with International Organization for Standardization (ISO) 27001. This research demonstrates an examination of the reasons that force enterprises to make a investment in ISO 27001 in addition to the incentives that might be acquired from having undergone this process. In addition, the research examines the reasons that push firms to make an investment in ISO 27001. More particularly, the research investigates an international IT consulting services institution that is responsible for the implementation of large-scale business assistance insertion and projects. It demonstrates the risk management framework and the administrative structure of the appropriate situations so that its procedures are adequate and also in line with the guidelines founded by ISO 27001. In conclusion, it discusses the problems and difficulties that were experienced.
Mobile-Based Training and Certification Framework for Teachers’ Professional Development

This study highlights the challenges faced in providing professional development programs for teachers, including limited resources and conventional training methods. Mobile technology, with its flexibility and cost-effectiveness, is increasingly being used as a part of professional development programs, but existing frameworks do not adequately support the integration of technology. This study proposes a mobile-based training certification framework that includes support for mobile phones, a training curriculum and assessment, expert support, and CPD evaluation. The framework was piloted with 35 participants from Pakistan and Saudi Arabia, with positive results indicating that mobile-based training is effective in enhancing teachers’ learning outcomes. According to the teachers’ responses, 100% agreed that this was a groundbreaking endeavor and expressed excitement to use it. Additionally, 98% found mobile learning easier and more enjoyable than traditional methods. The study recommends the implementation of the mobile-based training certification framework to improve teachers’ professional development and bridge the gap between traditional training methods and the needs of modern-day educators. The study also highlights the potential of mobile-based training to improve student outcomes by developing the knowledge and skills of teachers.
SR-HGN: Semantic- and Relation-Aware Heterogeneous Graph Neural Network

Graph Neural Networks (GNNs) have received considerable attention in recent years due to their unique ability to model both topologies and semantics in the graphs. In this paper, we explore the use of GNNs in learning low-dimensional node representations in Heterogeneous Information Networks (HINs), which retain rich semantic information across multiple types of nodes and relations. Existing methods for HINs generally focus on modeling heterogeneity at either the node level or the relation level, but not both. As a result, these methods often fall short of optimal performance. To address this issue, we propose a novel Semantic- and Relation-aware Heterogeneous Graph neural Network, dubbed SR-HGN, which jointly incorporates rich semantics preserved on nodes and relations. Our approach involves projecting the HINs into a low-dimensional vector space through two steps: node-level aggregation and type-level aggregation. The node-level aggregation employs an attention mechanism to create relation vectors by aggregating messages from neighborhoods connected via the same type of relation. The type-level aggregation leverages relation vectors to aggregate node representations. In particular, we introduce semantic-aware attention and relation-aware attention in the type-level aggregation to model the contributions of relation vectors, in order to simultaneously gain knowledge from node semantics and relational information. Unlike other approaches that rely on pre-defined meta-paths, our model can be readily applied to most real-world applications without requiring any domain knowledge. To validate the effectiveness of our proposed approach, we conducted extensive experiments on three public datasets. Experimental results demonstrate that the SR-HGN significantly outperforms state-of-the-art baselines on node classification and node clustering tasks.
A novel devaney chaotic map with uniform trajectory for color image encryption

The design of chaotic image encryption algorithms has become a hotspot. This paper first proposes a novel Devaney chaotic map with uniform trajectories to overcome the problems of weak chaotic behaviors and uneven trajectory distribution of the existing chaotic systems. We prove that our chaotic map possesses the three principal features of the Devaney definition and preserves uniform trajectory distribution through symbolic dynamics and the probability theory. In addition, simulation experiments show that our chaotic map has the advantages of fast efficiency and high security. Then, we design a color image encryption algorithm, which uses our Devaney chaotic map to generate digital chaotic pseudo-random sequences, and improves the confusion and diffusion structure of existing image encryption algorithms from the perspectives of pixels and planes. Although our algorithm does not use the hash value of plain images, it achieves excellent diffusion and confusion ability. Therefore, it avoids the failure of decryption caused by the tampering of hash value in the transmission process. Experimental results show that our image encryption algorithm can well resist probabilistic attacks and differential attacks.
Hybrid multi-group stochastic cooperative particle swarm optimization algorithm and its application to the photovoltaic parameter identification problem

The accurate estimation of model parameters is significant for the simulation, evaluation, control, and optimization of photovoltaic systems. Recently, meta-heuristic algorithms(MHAs) have been proposed to solve the photovoltaic parameter identification problem. However, extracting the accurate and reliable parameters of PV models is still a great challenge, and many HMAs may present unsatisfactory performance due to their premature or slow convergence. Therefore, how to develop algorithms efficiently balancing the exploration and exploitation to improve the accuracy and reliability of the algorithm is extremely important. This paper proposes a Hybrid multi-group stochastic cooperative optimization algorithm (HMSCPSO). In the proposed algorithm, we designed a multi-group cooperation search mechanism to enhance the global search capability: Each group utilized different strategies. The first group used classic velocity and position updates, the second group employed the chaos strategy, and the third group utilized the lévy flight strategy. Through cooperation search between groups to increase the diversity of the population and reduce the possibility of falling into local optimum, but also concentrate some individuals to explore the current global optimum to improve the accuracy of the solution. HMSCPSO and its variants were tested on 27 benchmark functions to verify the proposed algorithm's effectiveness. Then, the HMSCPSO is applied to solve four PV parameter identification problems of different photovoltaic models. Statistical experiment results demonstrate that the proposed algorithm has excellent advantages compared with other meta-heuristic algorithms in terms of accuracy, reliability, and convergence speed. Therefore, HMSCPSO is expected to be an effective parameter identification method for solar cells and photovoltaic modules.
A K-SVD Based Compressive Sensing Method for Visual Chaotic Image Encryption

The visually secure image encryption scheme is an effective image encryption method, which embeds an encrypted image into a visual image to realize a secure and secret image transfer. This paper proposes a merging compression and encryption chaos image visual encryption scheme. First, a dictionary matrix D is constructed with the plain image by the K-SVD algorithm, which can encrypt the image while sparsing. Second, an improved Zeraoulia-Sprott chaotic map and logistic map are employed to generate three S-Boxes, which are used to complete scrambling, diffusion, and embedding operations. The secret keys of this scheme contain the initial value of the chaotic system and the dictionary matrix D, which significantly increases the key space, plain image correlation, and system security. Simulation shows the proposed image encryption scheme can resist most attacks and, compared with the existing scheme, the proposed scheme has a larger key space, higher plain image correlation, and better image restoration quality, improving image encryption processing efficiency and security.
Novel chaos based cryptosystem using four-dimensional hyper chaotic map with efficient permutation and substitution techniques

In this paper, a competent hyper chaos-based image encryption technique is proposed. The cryptosystem uses a four-dimensional hyper chaotic map for implementing the secret key information. In terms of Lyapunov exponents, NIST test results and bifurcation diagrams, 4-D hyperchaotic system exhibits better dynamical behavior than lower dimensional chaotic systems. Also, 4-D map has superior hyperchaotic performance in views of ergodicity, complexity, and randomness behavior and is capable to generate an unpredictable keystream, which is highly suitable for encryption. Therefore, a novel image encryption algorithm using four-dimensional hyper chaotic system is proposed here. A novel method with sequence of mathematical operations is used for the secret key generation. Both the confusion and diffusion phases are implemented on the basis of this secrete random values with innovative designs. In permutation phase, keystream-based pixel level and bit level scrambling are performed to shuffle the image pixels effectively. Novel methods are implemented to accomplish pixel level scrambling with row and column shift operations. The bit level scrambling is also instigated with a novel cyclic rotation technique. Thus, the pixel level and bit level scrambling ensure higher security in the permutation process. In substitution phase, the keystream is XORed with scrambled images in an effective manner to obtain the final encrypted image. Various evaluation measures are used to access the safety and security of the proposed system. The evaluation result of the projected method with numerous other techniques shows the sovereignty and higher security of the planned technique. The keystreams are generated from the hyperchaotic Lorenz system using a novel approach, and its generation is completely independent of the original image. The algorithm has wider application prospects, including encryption of images in medical field and cloud environment.
SAR ship detection based on improved YOLOv5 and BiFPN

Synthetic aperture radar (SAR) is an advanced microwave sensor widely used in ocean monitoring, whose operation is not affected by light and weather. Ship targets in SAR images contain characteristically unclear contour information, a complex background, and display strong scattering. Ship detection algorithms based on convolutional neural networks achieved good results, albeit with many missed and false detections. To address this issue, we propose an improved scheme based on YOLOv5, that combines coordinate attention blocks and uses a bidirectional feature pyramid network for better feature fusion. Experimental results obtained with SAR images datasets demonstrate the effectiveness and applicability of the proposed model when applied for ship detection in SAR images. Compared to the original YOLOv5, the detection accuracy of the proposed method was increased from 81.28% to 88.27%, and the mean average precision was increased from 92.57% to 95.02%, which showed significant performance improvement by the proposed method in terms of detection accuracy and speed.
The appropriation of blockchain implementation in the supply chain of SMES based on fuzzy LMAW

This study investigates the adoption of blockchain technology in small and medium-sized enterprises’ (SMEs) supply chains. Blockchain has been deemed a “Game Changer” for its potential benefits but also has several limitations due to its properties and SMEs’ limited resources. Small and medium-sized companies (SMEs) have more problems employing Industry 4.0 Technologies in practice than larger companies. So, the employment of BC in SMEs’ supply chains, as the economy’s backbone, deserves a full investigation which has been addressed in this paper. We use the Technology–Organization–Environment framework to examine both positive and negative factors affecting blockchain adoption in SMEs. A Delphi method was used to refine a list of 42 factors down to the 22 most essential ones. These factors were then prioritized using the Fuzzy Logarithmic Additive Weights Methodology based on expert preferences. The findings indicate that while financial factors play a crucial role in adoption, transparency, and traceability are significant benefits that motivate organizations to adopt blockchain. The organizational dimension also has a substantial impact on decision-making. In this study, we conclude that SMEs must carefully consider their circumstances before adopting blockchain technology, given its infancy state. The barriers to adoption are just as significant as incentives, and SMEs must weigh the financial and organizational factors before making the decision. This study extends the literature by comprehensively looking at all possible factors influencing BC adoption by SMEs. Furthermore, we have highlighted valuable insights for policymakers and SME owners to make the best decision.
Detection and Localization of Arabic Text in Natural Scene Images

Text identification in natural scenes has emerged as a major research area in computer vision., with several applications including video retrieval and quick translation systems. Text identification is difficult in natural scene images due to changes in text size, color, orientation, background, contrast, and resolution. Text detection in complex backgrounds is more important to us. To address this issue, this work provides a detection and verification approach based on You Only Look Once Yolo (YOLO) algorithm. To get more accurate end findings, we use the YOLO-v4-Tiny approach to develop our custom text detection model. To evaluate the system, we constructed a ground verification system. Our method obtained 94.85% precision for experimental data.
Data-Driven Modeling of Mach-Zehnder Interferometer-Based Optical Matrix Multipliers

Photonic integrated circuits are facilitating the development of optical neural networks, which have the potential to be both faster and more energy efficient than their electronic counterparts since optical signals are especially well-suited for implementing matrix multiplications.However, accurate programming of photonic chips for optical matrix multiplication remains a difficult challenge.Here, we describe both simple analytical models and data-driven models for offline training of optical matrix multipliers.We train and evaluate the models using experimental data obtained from a fabricated chip featuring a Mach-Zehnder interferometer mesh implementing 3-by-3 matrix multiplication.The neural network-based models outperform the simple physics-based models in terms of prediction error.Furthermore, the neural network models are also able to predict the spectral variations in the matrix weights for up to 100 frequency channels covering the C-band.The use of neural network models for programming the chip for optical matrix multiplication yields increased performance on multiple machine learning tasks.
Human Activity Recognition Based on Deep-Temporal Learning Using Convolution Neural Networks Features and Bidirectional Gated Recurrent Unit With Features Selection

Recurrent Neural Networks (RNNs) and their variants have been demonstrated tremendous successes in modeling sequential data such as audio processing, video processing, time series analysis, and text mining. Inspired by these facts, we propose human activity recognition technique to proceed visual data via utilizing convolution neural network (CNN) and Bidirectional-gated recurrent unit (Bi-GRU). Firstly, we extract deep features from frames sequence of human activities videos using CNN and then select most important features from the deep appearances to improve performance and decrease computational complexity of the model. Secondly, to learn temporal motions of frames sequence, we design Bi-GRU and feed those deep-important features extracted from frames sequence of human activities to Bi-GRU which learn temporal dynamics in forward and backward direction at each time step. We conduct extensive experiments on realistic videos of human activity recognition datasets YouTube11, HMDB51 and UCF101. Lastly, we compare the obtained results with existing methods to show the competence of our proposed technique.
Readability Indices Do Not Say It All on a Text Readability

We propose a universal readability index, GU, applicable to any alphabetical language and related to cognitive psychology, the theory of communication, phonics and linguistics. This index also considers readers’ short-term-memory processing capacity, here modeled by the word interval IP, namely, the number of words between two interpunctions. Any current readability formula does not consider Ip, but scatterplots of Ip versus a readability index show that texts with the same readability index can have very different Ip, ranging from 4 to 9, practically Miller’s range, which refers to 95% of readers. It is unlikely that IP has no impact on reading difficulty. The examples shown are taken from Italian and English Literatures, and from the translations of The New Testament in Latin and in contemporary languages. We also propose an extremely compact formula, relating the capacity of human short-term memory to the difficulty of reading a text. It should synthetically model human reading difficulty, a kind of “footprint” of humans. However, further experimental and multidisciplinary work is necessary to confirm our conjecture about the dependence of a readability index on a reader’s short-term-memory capacity.
Artificial intelligence for clinical decision support for monitoring patients in cardiovascular ICUs: A systematic review

Artificial intelligence (AI) and machine learning (ML) models continue to evolve the clinical decision support systems (CDSS). However, challenges arise when it comes to the integration of AI/ML into clinical scenarios. In this systematic review, we followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA), the population, intervention, comparator, outcome, and study design (PICOS), and the medical AI life cycle guidelines to investigate studies and tools which address AI/ML-based approaches towards clinical decision support (CDS) for monitoring cardiovascular patients in intensive care units (ICUs). We further discuss recent advances, pitfalls, and future perspectives towards effective integration of AI into routine practices as were identified and elaborated over an extensive selection process for state-of-the-art manuscripts.Studies with available English full text from PubMed and Google Scholar in the period from January 2018 to August 2022 were considered. The manuscripts were fetched through a combination of the search keywords including AI, ML, reinforcement learning (RL), deep learning, clinical decision support, and cardiovascular critical care and patients monitoring. The manuscripts were analyzed and filtered based on qualitative and quantitative criteria such as target population, proper study design, cross-validation, and risk of bias.More than 100 queries over two medical search engines and subjective literature research were developed which identified 89 studies. After extensive assessments of the studies both technically and medically, 21 studies were selected for the final qualitative assessment.Clinical time series and electronic health records (EHR) data were the most common input modalities, while methods such as gradient boosting, recurrent neural networks (RNNs) and RL were mostly used for the analysis. Seventy-five percent of the selected papers lacked validation against external datasets highlighting the generalizability issue. Also, interpretability of the AI decisions was identified as a central issue towards effective integration of AI in healthcare.
The Way to Make Blind People Use the Email System: Voice Based Email Generating System Using Artificial Intelligence

People are becoming used to fake world and vocal communication as technology advances. There are a few ways to communicate with people online in this modern invention. A large number of individuals often choose and use the simplest form of reporting, namely email. The era of encrypted email allows users to communicate with other people by posting signals and also facilitates cross-border corporate communication. Some individuals are unable to take advantages of this technology due to their ignorant or lack the necessary visible screen ability. Therefore, a Speech completely messaging device is suggested using Py but instead Ai to save time for externally examined people. Its device gives those who have been evaluated on the outside some power of contact and considerably increases their sense of stability as objectivity. With the use of that invention, blind persons will indeed be able to send out emails just like other regular citizens. Voice-based messaging systems use cutting-edge technology to ensure their legitimacy to people who have been vetted on the outside.