Reviewerly/exHarmony huggingface dataset:
Shallow Fully Connected Neural Network Training by Forcing Linearization into Valid Region and Balancing Training Rates

A new supervisory training rule for a shallow fully connected neural network (SFCNN) is proposed in this present study. The proposed training rule is developed based on local linearization and analytical optimal solutions for linearized SFCNN. The cause of nonlinearity in neural network training is analyzed, and it is removed by local linearization. The optimal solution for the linearized SFCNN, which minimizes the cost function for the training, is analytically derived. Additionally, the training efficiency and model accuracy of the trained SFCNN are improved by keeping estimates within a valid range of the linearization. The superiority of the proposed approach is demonstrated by applying the proposed training rule to the modeling of a typical nonlinear pH process, Boston housing prices dataset, and automobile mileage per gallon dataset. The proposed training rule shows the smallest modeling error and the smallest iteration number required for convergence compared with several previous approaches from the literature for the case study.
Neuromorphic processing and computing based on wavelength-division multiplexing

&lt;p&gt; Optical neural networks (ONNs), or optical neuromorphic hardware accelerators, have the potential to dramatically enhance the computing power and energy efficiency of mainstream electronic processors, due to their ultra-large bandwidths of up to 10’s of terahertz together with their analog architecture that avoids the need for reading and writing data back-and-forth. Different multiplexing techniques have been demonstrated to demonstrate ONNs, amongst which wavelength-division multiplexing (WDM) techniques make sufficient use of the unique advantages of optics in terms of broad bandwidths. Here, we review recent advances in WDM-based ONNs, focusing on methods that use integrated microcombs to implement ONNs. We present results for human image processing using an optical convolution accelerator operating at 11 Tera operations per second. The open challenges and limitations of ONNs that need to be addressed for future applications are also discussed. &lt;/p&gt;
Scrutinizing the Trends and Indexes of the Knowledge Economy in the Kingdom of Saudi Arabia

This study focusses on the concepts of significance to the knowledge economy and its characteristics in the Kingdom of Saudi Arabia. Qualitative and quantitative research methodology design has been incorporated to validate the study. The outcome of the study exhibited that the Kingdom of Saudi Arabia has achieved progress according to the research and development index. The rate of expenditure as a ratio from the Gross Domestic Product is still negligent and modest in comparison with the international expenditure rates. It has been recommended to adopt an effective mechanism for the consolidation of the relations between the scientific research institutions and the productive and service sectors. &#x0D; Keywords: Knowledge economy, digital economy, human development, information technology, Kingdom of Saudi Arabia.&#x0D;
Blockchain Traceability System in Complex Application Scenarios: Image-Based Interactive Traceability Structure

To solve the problems exposed by the application of blockchain technology under complex scenarios, such as fraudulent use of data, hard to store huge amounts of data, and low traceability efficiency under an ultra-huge number of traceability requests, this paper constructs an image-based interactive traceability structure by using images as an enhancement. By adding pointers to raw image files, a specific file structure is formed for traceability, and the traceability process is separated from the verification process, therefore realizing the distributed traceability of “traceability off the chain and verification on the chain”. The experimental results show that, compared with the traditional blockchain traceability mode, the interactive traceability structure can reduce the data retrieval pressure and greatly improve the traceability efficiency of a specific transaction chain. With the growth of the span of the transaction chain, the traceability efficiency advantage of the interactive traceability structure becomes more obvious.
Mushroom Cultivation for Increasing Income and Sustainable Development of Small and Marginal Farmers

A mushroom is the fleshy and spore-bearing fruiting body of a fungus and belongs to the class Basidiomycetes. Mushroom cultivation is a low-cost, high-return activity that has proven to be an additional source of income. Because the vegetarian population in our country's urban centers has adopted mushrooms in their diet due to their nutritious worth, mushroom producers in nearby rural areas have an excellent market for selling their products quickly and at a reasonable price. As a result, the mushroom benefits both producers and consumers, earning it the title of super food. Mushroom production can also be used to empower rural people, offer additional income for agricultural families, and address the issue of rural people's nutritional needs. Rural unemployed people are the most productive workforce in the economy, necessitating efforts to enhance and strengthen their socioeconomic condition. Mushroom farming all year can produce revenue from the sale of spawns as well as the sale of fresh mushrooms in a sustainable manner by using farm by-products as a raw material.
Six methods for transforming layered hypergraphs to apply layered graph layout algorithms

Abstract Hypergraphs are a generalization of graphs in which edges (hyperedges) can connect more than two vertices—as opposed to ordinary graphs where edges involve only two vertices. Hypergraphs are a fairly common data structure but there is little consensus on how to visualize them. To optimize a hypergraph drawing for readability, we need a layout algorithm. Common graph layout algorithms only consider ordinary graphs and do not take hyperedges into account. We focus on layered hypergraphs, a particular class of hypergraphs that, like layered graphs, assigns every vertex to a layer, and the vertices in a layer are drawn aligned on a linear axis with the axes arranged in parallel. In this paper, we propose a general method to apply layered graph layout algorithms to layered hypergraphs. We introduce six different transformations for layered hypergraphs. The choice of transformation affects the subsequent graph layout algorithm in terms of computational performance and readability of the results. Thus, we perform a comparative evaluation of these transformations in terms of number of crossings, edge length, and impact on performance. We also provide two case studies showing how our transformations can be applied to real‐life use cases. A copy of this paper with all appendices and supplemental material is available at osf.io/grvwu.
Retracted: Contrast and Synthetic Multiexposure Fusion for Image Enhancement

[This retracts the article DOI: 10.1155/2021/2030142.].
Inception-LSTM Human Motion Recognition with Channel Attention Mechanism

An improved channel attention mechanism Inception-LSTM human motion recognition algorithm for inertial sensor signals is proposed to address the problems of high cost, many blind areas, and susceptibility to environmental effects in traditional video image-oriented human motion recognition algorithms. The proposed algorithm takes the inertial sensor signal as input, first extracts the spatial features of the sensor signal into the feature vector graph from multiple scales using the Inception parallel convolution structure, then uses the improved ECA (Efficient Channel Attention) channel attention module to extract the critical details of the feature vector graph of the sensor data, and finally uses the LSTM network to further extract the temporal features of the inertial sensor signals to achieve the classification and recognition of human motion posture. The experiment results demonstrate that 95.04% recognition accuracy on the public dataset PAMAP2 and 98.81% accuracy on the self-built dataset can be realized based on the algorithm model, indicating that the algorithm model has a superior recognition effect. In addition, the results of the visual analysis of channel attention weights show that the proposed model is interpretable for the recognition of human motions and is consistent with the living intuition.
Financial Development and Female Labor Income Share: Evidence from Global Data

While there has been sizable literature on the effect of financial development on growth, inequality, and poverty, there are fewer studies on its impact on female labor force participation or women’s wellbeing. Using a novel dataset, this paper investigates the association between the dimensions of financial development and female labor income share for 156 countries for the period of 1991-2019 to contribute to the literature on the role of financial development in improving women’s wellbeing. The findings show that financial development is positively associated with women’s income in high-income countries but not in low-income countries. The main implication of the study is that financial development in poor countries is not sufficiently inclusive enough to create economic opportunities for women.
Point-Wise Phase Estimation Method in Fringe Projection Profilometry under Non-Sinusoidal Distortion

In fringe projection profilometry, high-order harmonics information of distorted fringe will lead to errors in the phase estimation. In order to solve this problem, a point-wise phase estimation method based on a neural network (PWPE-NN) is proposed in this paper. The complex nonlinear mapping relationship between the gray values and the phase under non-sinusoidal distortion is constructed by using the simple neural network model. It establishes a novel implicit expression for phase solution without complicated measurement operations. Compared with the previous method of combining local image information, it can accurately calculate each phase value by point. The comparison results show that the traditional method is with periodic phase errors, while the proposed method can effectively eliminate phase errors caused by non-sinusoidal phase shifting.
Neuromorphic Computing Based on Wavelength-Division Multiplexing

Optical neural networks (ONNs), or optical neuromorphic hardware accelerators, have the potential to dramatically enhance the computing power and energy efficiency of mainstream electronic processors, due to their ultra-large bandwidths of up to 10&amp;rsquo;s of terahertz together with their analog architecture that avoids the need for reading and writing data back-and-forth. Different multiplexing techniques have been demonstrated to demonstrate ONNs, amongst which wavelength-division multiplexing (WDM) techniques make sufficient use of the unique advantages of optics in terms of broad bandwidths. Here, we review recent advances in WDM-based ONNs, focusing on methods that use integrated microcombs to implement ONNs. We present results for human image processing using an optical convolution accelerator operating at 11 Tera operations per second. The open challenges and limitations of ONNs that need to be addressed for future applications are also discussed.
When is the Evaluation of Extended CRPQ Tractable?

We investigate the complexity of the evaluation problem for ECRPQ: Conjunctive Regular Path Queries (CRPQ), extended with synchronous relations (aka regular or automatic). We give a characterization for the evaluation and parameterized evaluation problems of ECRPQ in terms of the underlying structure of queries. As we show, complexity can range between PSpace, NP and polynomial time for the evaluation problem, and between XNL, W[1], and FPT for parameterized evaluation.
Design of a Panoramic Scanning Device Based on a Piezoelectric Ceramic Stack Actuator and Friction Transmission

In view of the complex structure and inaccurate positioning capabilities of the existing panoramic scanning system, a panoramic scanning device based on a piezoelectric ceramic stack actuator and friction transmission was designed. In our model, the output displacement of the piezoelectric ceramics is amplified by a bridge-type flexure hinge and applied to a shaft by friction to achieve panoramic scanning imaging. The mathematical model of the device was established, and the working characteristics were analyzed. The analysis demonstrated that the linear displacement increment of the shaft is a superposition. A modality simulation was performed, and the simulation results show that the designed device works normally at a frequency of 511.5 Hz. The test results indicated that the displacement of the actuator can reach 6 μm at an input voltage of 100 V. Moreover, the laser scanning results showed that the designed device can perform panoramic scanning imaging, which meets the requirements for use on the high-speed imaging system.
Are Deep Models Robust against Real Distortions? A Case Study on Document Image Classification

Deep neural networks have been extensively researched in the field of document image classification to improve classification performance and have shown excellent results. However, there is little research in this area that addresses the question of how well these models would perform in a real-world environment, where the data the models are confronted with often exhibits various types of noise or distortion. In this work, we present two separate benchmark datasets, namely RVL-CDIP-D and Tobacco3482-D, to evaluate the robustness of existing state-of-the-art document image classifiers to different types of data distortions that are commonly encountered in the real world. The proposed benchmarks are generated by inserting 21 different types of data distortions with varying severity levels into the well-known document datasets RVL-CDIP and Tobacco3482, respectively, which are then used to quantitatively evaluate the impact of the different distortion types on the performance of latest document image classifiers. In doing so, we show that while the higher accuracy models also exhibit relatively higher robustness, they still severely underperform on some specific distortions, with their classification accuracies dropping from ~90% to as low as ~40% in some cases. We also show that some of these high accuracy models perform even worse than the baseline AlexNet model in the presence of distortions, with the relative decline in their accuracy sometimes reaching as high as 300-450% that of AlexNet. The proposed robustness benchmarks are made available to the community and may aid future research in this area.
Urdu News Content Classification Using Machine Learning Algorithms

As the world has become a global village, the flow of news in terms of volume and speed increases. It is necessary to engage computing machines for assisting people in dealing with this massive data. The availability of different types of news and such material on the Internet serves as a source of information for billions of users. Millions of people in our subcontinent speak and understand Urdu. There are several classification techniques that are available and are applied to classify English news like political, Education, Medical, etc. Plenty of research work has been done in multiple languages but Urdu is still to be worked on due to a lack of resources. This research evaluates the performance of twelve (12) different Machine learning classifiers for the Urdu News text Classification problem. The analysis was performed on a relatively big and recent collection of Urdu text that contains over 0.15 million (153,050) labeled instances of eight different classes. In addition, after applying pre-processing techniques, the TF-IDF weighting technique was adopted for feature selection and data extraction. After evaluating various machine learning methods, the SVM outperforms the other eleven algorithms with an accuracy of 91.37 %. We also compare its results with other classifiers like linear SVM, Logistic regression, SGD, Naïve bays, ridge regression, and a few others.
An Artificial Intelligence Conversational Chatbot Developed for Non-Native English Speakers

As common knowledge would dictate, the best way to learn a language is to talk with native speakers of said language. However, it may not be the easiest for non-fluent, non-native speakers of languages such as English to find native English speakers to converse with. This paper proposes a conversational chatbot that would help non-native speakers to converse with artificial intelligence with equal, if not greater, fluency compared with a native English speaker.
Automation of reversible steganographic coding with nonlinear discrete optimisation

Authentication mechanisms are at the forefront of defending the world from various types of cybercrime. Steganography can serve as an authentication solution through the use of a digital signature embedded in a carrier object to ensure the integrity of the object and simultaneously lighten the burden of metadata management. Nevertheless, despite being generally imperceptible to human sensory systems, any degree of steganographic distortion might be inadmissible in fidelity-sensitive situations such as forensic science, legal proceedings, medical diagnosis and military reconnaissance. This has led to the development of reversible steganography. A fundamental element of reversible steganography is predictive analytics, for which powerful neural network models have been effectively deployed. Another core element is reversible steganographic coding. Contemporary coding is based primarily on heuristics, which offers a shortcut towards sufficient, but not necessarily optimal, capacity--distortion performance. While attempts have been made to realise automatic coding with neural networks, perfect reversibility is unattainable via such learning machinery. Instead of relying on heuristics and machine learning, we aim to derive optimal coding by means of mathematical optimisation. In this study, we formulate reversible steganographic coding as a nonlinear discrete optimisation problem with a logarithmic capacity constraint and a quadratic distortion objective. Linearisation techniques are developed to enable iterative mixed-integer linear programming. Experimental results validate the near-optimality of the proposed optimisation algorithm when benchmarked against a brute-force method.
Mathematical Model of Decision-Making on Data Fuzzy Set in Logistics Field

Insufficiently effective management of transportation process is associated with the cases of irregularity and inconsistency of the work of transport various modes among themselves, insignificant development of processing capacities for cargo transshipment between transport modes as well as of insufficient number of terminals serving to «smooth» work inconsistency in time in transport flows. This article describes mathematical model for choosing a rational variant of cargo delivery from the position of a transportation client. The main advantage of the described mathematical model is the minimization of production and technological costs on the platform of fuzzy set theory with an “eye” on exact sets (topology on the set of parameters of real numbers R1). Sufficiently smooth function f ( x), x ∈ R1, is adopted. For it, the Taylor representation takes place locally at the point x0. Presented in the paper integrated approach allows to evaluate the rationality of routes and the choice of the best among them which takes into account wide range of criteria, limitations of transport parameters and cargo owner requirements. It is worth to note that the rationality of choice is sometimes formed not only from the best result, but also from emerging alternatives and factors affecting this or that choice and process. A client chooses a competitive route option according to the variation of claimed by him demands on logistics projects. The paper also presents existing method analysis on reveal of the rationalization of transport constituent in logistics field.
A Hybrid Cryptography and End-to-end Security Model for Wireless Sensor Netwoks

Abstract Wireless Sensor Network (WSN) is an interconnected and infrastructure-less wireless network. It is deployed in many wireless sensors in an ad-hoc manner to monitor the system, physical or environmental conditions. Wireless sensor networks are used to develop the applications in healthcare, agriculture, vehicle network, defense, etc. Due to this network's need to provide proper security techniques to ensure secure communication. The hybrid cryptographic approach is suitable for WSN because it can take care of sensor's challenges like computation power, storage capability and energy. Symmetric-key cryptography is used to track only one key for the encryption and decryption process. So its computation speed is high. An asymmetric key is used to share information in a very secure way. Both can provide more security against attacks in wireless networks. A new two-phase cryptographic technique has been developed in this work that uses both symmetric and asymmetric methods. In the proposed algorithm, advanced encryption standards (AES) and Rivest–Shamir–Adleman (RSA) are used in different phases of the algorithm. Because symmetric key cryptographic techniques provide a high level of security, and asymmetric key cryptography provides easier key management. phase 1 has been developed using the AES algorithm. In phase 2, RSA has been used, and all phases are executed in parallel. The proposed algorithm provides better encryption, decryption and total execution time when compared to the existing algorithms.
Cyber Crime in Business: Review Paper

The criminals of cyber, nowadays, are exploiting software that aims to damage or disable computer systems, i.e., malware, viruses, bots and other forms of complicated attacks or threats, to breach considerable organizations.Those threats could be for various goals, such as financial revenue, disruption of business, or political trend.The situation used by the cybercriminals is to attack diverse organizations and multiple targets for raising the likelihood ratio of success.It could be spread the viruses and malware programs that designed on daily basis.The influence of cybercrimes in business is shown in this paper by surveying some studies which were conducted in some countries.