Reviewerly/exHarmony huggingface dataset:
Cloud Statistics of Accounting Informatization Based on Statistics Mining

With the rapid development of information technology, the amount of all kinds of data information is increasing rapidly. As an important means to collect, store, and manage massive data, and then analyze and predict the habits and characteristics of certain groups of people and even the development trend of a certain industry, big data technology provides a comprehensive strategic basis for management decision makers that the traditional processing mode cannot match. Contemporary management accounting serves the whole process of enterprise internal control, so it will produce a large number of various data. With the explosive growth of network data and the increasing scale of database, more and more people begin to study data mining, and the classification algorithm, as the key technology in data mining, has also received extensive attention. In order to further improve the information technology level of enterprise management accounting and increase the depth of information application, many enterprises began to pay more attention to data mining, and through deep data mining, the depth and breadth of enterprise data analysis were improved. In the research of data and accounting informatization, data mining technology accounts for about 50% of informatization, which is the way for future development. With the advent of the information age, the dependence of enterprises on information technology in the process of accounting management has been further improved. If enterprises want to achieve better development in the information age, they need to pay more attention to the information technology of management accounting and improve the application ability of enterprise staff in information.
IPCRF: An End-to-End Indian Paper Currency Recognition Framework for Blind and Visually Impaired People

Currency recognition has always been a troublesome task for blind and visually impaired people (BVIP). The problem is more severe in developing countries such as India, where there is still a lack of robust currency recognition systems. BVIP primarily relies on size variations and patterns such as intaglio printings for recognizing the underlying currency denominations. Most of the current Indian legal tenders resemble in size, thus making the identification process more strenuous. Also, the engraved patterns are not as distinctive as BVIP standards, and they fade over time. For an automated paper currency recognition system, issues such as folded or partial views, uneven illumination, and background clutter make it non-trivial and challenging. This paper ventures to present an end-to-end and robust framework for assisting BVIP in recognizing the Indian paper currency denomination. This paper presents a lightweight network, IPCRNet, useful in a resource-constrained environment such as low/medium level smartphones. The proposed network is based on Dense connection, Multi-Dilation, and Depth-wise separable convolution layers. Additionally, we congregated one of the most diversified Indian paper currency image dataset with more than 50,000 images belonging to almost all denominations in circulation. A customized and publically available android application, &#x201C;Roshni-Currency recognizer&#x201D;, has also been introduced. The experimental results on multiple datasets demonstrate the superiority of the proposed model. IPCRNet improves the classification accuracy by more than 2% on the proposed dataset compared to the state-of-the-art networks.
RQ Labs: A Cybersecurity Workforce Skills Development Framework

This research contributes to the knowledge of how Information Systems (IS) researchers can iteratively intervene with practitioners to co-create instructional programs with a framework designed for fast-paced, rapidly changing IS fields such as cybersecurity. We demonstrate how complex fields, such as cybersecurity, have the need for a skilled workforce that continues to rapidly outpace supply from universities. IS researchers partnering with practitioners can use this research as an exemplar of a method to design, build, and evaluate these innovative co-curricular IS programs. Moreover, we find these co-curricular IS programs are essential to upskilling students, integrating training on the latest tools, systems, and processes in these rapidly evolving disciplines.
RETRACTED: Fuzzy theory and method for evaluating the importance of college course teaching for future education

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219329.
Two-stream fusion model using 3D-CNN and 2D-CNN via video-frames and optical flow motion templates for hand gesture recognition

Hand gestures are useful tools for many applications in the human-computer interaction community. Here, the objective is to track the movement of the hand irrespective of the shape, size and color of the hand. And, for this, a motion template guided by optical flow (OFMT) is proposed. OFMT is a compact representation of the motion information of a gesture encoded into a single image. Recently, deep networks have shown impressive improvements as compared to conventional hand-crafted feature-based techniques. Moreover, it is seen that the use of different streams with informative input data helps to increase the recognition performance. This work basically proposes a two-stream fusion model for hand gesture recognition. The two-stream network consists of two layers—a 3D convolutional neural network (C3D) that takes gesture videos as input and a 2D-CNN that takes OFMT images as input. C3D has shown its efficiency in capturing spatiotemporal information of a video, whereas OFMT helps to eliminate irrelevant gestures providing additional motion information. Though each stream can work independently, they are combined with a fusion scheme to boost the recognition results. We have shown the efficiency of the proposed two-stream network on two databases.
Group Class Residual ℓ1-Minimization on Random Projection Sparse Representation Classifier for Face Recognition

Sparse Representation-based Classification (SRC) has been seen to be a reliable Face Recognition technique. The ℓ1 Bayesian based on the Lasso algorithm has proven to be most effective in class identification and computation complexity. In this paper, we revisit classification algorithm and then recommend the group-based classification. The proposed modified algorithm, which is called as Group Class Residual Sparse Representation-based Classification (GCR-SRC), extends the coherency of the test sample to the whole training samples of the identified class rather than only to the nearest one of the training samples. Our method is based on the nearest coherency between a test sample and the identified training samples. To reduce the dimension of the training samples, we choose random projection for feature extraction. This method is selected to reduce the computational cost without increasing the algorithm’s complexity. From the simulation result, the reduction factor (ρ) 64 can achieve a maximum recognition rate about 10% higher than the SRC original using the downscaling method. Our proposed method’s feasibility and effectiveness are tested on four popular face databases, namely AT&amp;T, Yale B, Georgia Tech, and AR Dataset. GCR-SRC and GCR-RP-SRC achieved up to 4% more accurate than SRC random projection with class-specific residuals. The experiment results show that the face recognition technology based on random projection and group-class-based not only reduces the dimension of the face data but also increases the recognition accuracy, indicating that it is a feasible method for face recognition.
Efficient Flower Text Entry in Virtual Reality

Text entry is a frequently used task in virtual reality (VR) applications, and controller is the most common interactive device in current VR systems. However, in terms of typing speed, there is still a gap between the existing controller-based text entry techniques and using a physical keyboard in reality, so it is important to improve the efficiency of the controller-based text entry. In this paper, we introduce Flower Text Entry, a single-controller text entry method based on a newly designed flower-shaped keyboard using hand 3D translation interaction for letters selection. We conduct user studies to optimize the keyboard design and the mapping between the interaction and selection, so as to evaluate our method. The results show that our method has high typing speed, lower error rate, and is very friendly to novices compared with the state-of-the-art controller-based text entry methods. After a short training, the novice group can type at 17.65 words per minute (WPM), and the potential expert group can type at 22.97 WPM. The highest typing speed is up to 30.80 WPM achieved by a potential expert participant.
A Robust Visual Tracking Method Based on Reconstruction Patch Transformer Tracking

Recently, the transformer model has progressed from the field of visual classification to target tracking. Its primary method replaces the cross-correlation operation in the Siamese tracker. The backbone of the network is still a convolutional neural network (CNN). However, the existing transformer-based tracker simply deforms the features extracted by the CNN into patches and feeds them into the transformer encoder. Each patch contains a single element of the spatial dimension of the extracted features and inputs into the transformer structure to use cross-attention instead of cross-correlation operations. This paper proposes a reconstruction patch strategy which combines the extracted features with multiple elements of the spatial dimension into a new patch. The reconstruction operation has the following advantages: (1) the correlation between adjacent elements combines well, and the features extracted by the CNN are usable for classification and regression; (2) using the performer operation reduces the amount of network computation and the dimension of the patch sent to the transformer, thereby sharply reducing the network parameters and improving the model-tracking speed.
An optimal elliptic curve cryptography based encryption algorithm for blockchain-enabled medical image transmission

In recent times, a number of Internet of Things (IoT) related healthcare applications have been deployed for automating healthcare services and offering easy accessibility to patients. Several issues like security, fault-tolerant, and reliability have restricted the utilization of IoT services in real-time healthcare environments. To achieve security, blockchain technology can be utilized which offers effective interoperability of healthcare databases, ease of medical data access, device tracking, prescription database, hospital assets, etc. Therefore, this paper presents an optimal Elliptic curve cryptography-based encryption algorithm for a blockchain-enabled medical image transmission model, named OECC-BMIT. The presented OECC-BMIT model involves different stages of operations such as encryption, optimal key generation, blockchain-enabled data transmission, and decryption. Firstly, the OECC-BMIT model performs Elliptic curve cryptography (ECC) based encryption technique to securely transmit the medical images. In order to generate the optimal set of keys for the ECC technique, modified bat optimization (MBO) algorithm is applied. Then, the encrypted images undergo secure transmission via blockchain technology. The encrypted images are decrypted on the recipient side and the original medical image is reconstructed effectively. Extensive sets of experimentations were performed to highlight the goodness of the OECC-BMIT algorithm and the obtained results pointed out the improved outcome over the state of art methods in terms of different measures.
Single-Trace Side-Channel Attacks on the Toom-Cook: The Case Study of Saber

The Toom-Cook method is a well-known strategy for building algorithms to multiply polynomials efficiently. Along with NTT-based polynomial multiplication, Toom-Cook-based or Karatsuba-based polynomial multiplication algorithms still have regained attention since the start of the NIST’s post-quantum standardization procedure. Compared to the comprehensive analysis done for NTT, the leakage characteristics of Toom-Cook have not been discussed. We analyze the vulnerabilities of Toom-Cook in the reference implementation of Saber, a third round finalist of NIST’s post-quantum standardization process. In this work, we present the first single-trace attack based on the soft-analytical side-channel attack (SASCA) targeting the Toom-Cook. The deep learning-based power analysis is combined with SASCA to decrease the number of templates since there are a large number of similar operations in the Toom-Cook. Moreover, we describe the optimized factor graph and improved belief propagation to make the attack more practical. The feasibility of the attack is verified by evaluation experiments. We also discuss the possible countermeasures to prevent the attack.
A novel scaled-gamma-tanh (SGT) activation function in 3D CNN applied for MRI classification

Abstract Activation functions in the neural network are responsible for ‘firing’ the nodes in it. In a deep neural network they ‘activate’ the features to reduce feature redundancy and learn the complex pattern by adding non-linearity in the network to learn task-specific goals. In this paper, we propose a simple and interesting activation function based on the combination of scaled gamma correction and hyperbolic tangent function, which we call Scaled Gamma Tanh (SGT) activation. The proposed activation function is applied in two steps, first is the calculation of gamma version as y = f ( x ) = ax α for x &lt; 0 and y = f ( x ) = bx β for x ≥ 0 , second is obtaining the squashed value as z = tanh( y ). The variables a and b are user-defined constant values whereas $$\alpha$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>α</mml:mi> </mml:math> and $$\beta$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>β</mml:mi> </mml:math> are channel-based learnable parameters. We analyzed the behavior of the proposed SGT activation function against other popular activation functions like ReLU, Leaky-ReLU, and tanh along with their role to confront vanishing/exploding gradient problems. For this, we implemented the SGT activation functions in a 3D Convolutional neural network (CNN) for the classification of magnetic resonance imaging (MRIs). More importantly to support our proposed idea we have presented a thorough analysis via histogram of inputs and outputs in activation layers along with weights/bias plot and t-SNE (t-Distributed Stochastic Neighbor Embedding) projection of fully connected layer for the trained CNN models. Our results in MRI classification show SGT outperforms standard ReLU and tanh activation in all cases i.e., final validation accuracy, final validation loss, test accuracy, Cohen’s kappa score, and Precision.
The role of artificial intelligence, marketing strategies, and organizational capabilities in organizational performance: The moderating role of organizational behavior

Currently, artificial intelligence and marketing strategies have become significant factors for improving the business capabilities that lead to improved business performance. Thus, the current study investigates the impacts of artificial intelligence and marketing strategies on the organizational performance of the tourism industry in the UAE. The present study also examines the mediating role of organizational capabilities in linking artificial intelligence, marketing strategies, and organizational performance of the tourism industry in the UAE. The analysis of the moderating impact of organizational behavior on the links between artificial intelligence, marketing strategies, and organizational performance are also part of the current study’s goals. This study used questionnaires to collect primary data from the respondents and analyzed it using Smart-PLS. The results indicated that artificial intelligence and marketing strategies have a positive association with the organizational performance of the tourism industry in the UAE. The outcomes also revealed that organizational capabilities positively mediate the links between artificial intelligence, marketing strategies, and organizational performance. The findings also demonstrated that organizational behavior significantly moderates the links between artificial intelligence, marketing strategies, and organizational performance of the tourism industry in the UAE. This study is relevant for regulators in supporting policy development related to artificial intelligence and marketing strategies for high organizational performance.
Relationship between Liquid-Based Cytology Preservative Solutions and Artificial Intelligence: Liquid-Based Cytology Specimen Cell Detection Using YOLOv5 Deep Convolutional Neural Network

&lt;b&gt;&lt;i&gt;Introduction:&lt;/i&gt;&lt;/b&gt; Deep learning is a subset of machine learning that has contributed to significant changes in feature extraction and image classification and is being actively researched and developed in the field of cytopathology. Liquid-based cytology (LBC) enables standardized cytological preparation and is also applied to artificial intelligence (AI) research, but cytological features differ depending on the LBC preservative solution types. In this study, the relationship between cell detection by AI and the type of preservative solution used was examined. &lt;b&gt;&lt;i&gt;Methods:&lt;/i&gt;&lt;/b&gt; The specimens were prepared from five preservative solutions of LBC and stained using the Papanicolaou method. The YOLOv5 deep convolutional neural network algorithm was used to create a deep learning model for each specimen, and a BRCPT model from five specimens was also created. Each model was compared to the specimen types used for detection. &lt;b&gt;&lt;i&gt;Results:&lt;/i&gt;&lt;/b&gt; Among the six models, a difference in the detection rate of approximately 25% was observed depending on the detected specimen, and within specimens, a difference in the detection rate of approximately 20% was observed depending on the model. The BRCPT model had little variation in the detection rate depending on the type of the detected specimen. &lt;b&gt;&lt;i&gt;Conclusions:&lt;/i&gt;&lt;/b&gt; The same cells were treated with different preservative solutions, the cytologic features were different, and AI clarified the difference in cytologic features depending on the type of solution. The type of preservative solution used for training and detection had an extreme influence on cell detection using AI. Although the accuracy of the deep learning model is important, it is necessary to understand that cell morphology differs depending on the type of preservative solution, which is a factor affecting the detection rate of AI.
Traditional African Dances Preservation Using Deep Learning Techniques

Human action recognition continues to evolve and improve through deep learning techniques. There have been studies with some success in the field of action recognition, but only a few of them have focused on traditional dance. This is because dance actions, especially in traditional African dance, are long and involve fast movements. This research proposes a novel framework that applies data science algorithms to the field of cultural preservation by applying various deep learning techniques to identify, classify, and model traditional African dances from videos. Traditional dances are an important part of African culture and heritage. Digital preservation of these dances in their multitude and form is a challenging problem. The dance dataset was constituted from freely available YouTube videos. Four traditional African dances were used for the dance classification process: Adowa, Swange, Bata, and Sinte dance. Five Convolutional Neural Network (CNN) models were used for the classification and achieved an accuracy between 93% and 98%. Additionally, human pose estimation algorithms were applied to Sinte dance. A model of Sinte dance that can be exported to other environments was obtained.
Simultaneous phase and amplitude modulation for dual-sensitivity profilometry of discontinuous objects

Fringe projection profilometry (FPP) is a well-known technique for digitizing solids. In FPP, straight fringes are projected over a digitizing solid, and a digital camera grabs the projected fringes. The sensitivity of FPP depends on the spatial frequency of the projected fringes. The projected fringes as seen by the camera are phase modulated by the surface of the digitizing object; the demodulated phase is usually wrapped. If the digitizing object has discontinuities larger than the fringe period, the phase jumps are lost. To preserve large phase discontinuities, one must use very low spatial frequency (low-sensitivity) fringes. The drawback of low-sensitivity FPP is that the demodulated phase has low signal-to-noise ratio (SNR). Much higher SNR is obtained by projecting shorter wavelength, at the cost of obtaining wrapped phase. A way out of this problem is to use dual-wavelength FPP (DW-FPP). In DW-FPP, two sets of projected fringes are used, one with long wavelength and another with shorter wavelength. Due to harmonics and gamma distortion, in DW-FPP, one usually needs four phase-shifted fringes for each sensitivity. Here we are proposing to combine the two sensitivities simultaneously, one coded in phase (PM) and the other coded in amplitude (AM), in order to obtain phase and amplitude modulated (DW-PAM) fringes. The low-sensitivity phase is coded as AM of the DW-PAM fringes. The main advantage of DW-PAM fringes is that one reduces the number of phase-shifted fringes by half: instead of using eight phase-shifted fringes (four for low and four for high sensitivities), one would need only four DW-PAM fringes. Of course, if one wants to increase the harmonic rejection of the recovered phase, one may use a higher order phase-shifting algorithm (PSA).
Telepathology in Nigeria for Global Health Collaboration

Inadequate pathology personnel and high cost of running a Pathology facility are factors affecting access to timely and quality pathology services in resource-constrained settings. Telepathology is a novel technology that allows Pathologists to remotely assess collected samples. Though the initial cost of setting up a telepathology facility is high, its overall benefits far outweigh the cost. Its usefulness as a quality assurance measure, as a permanent image data storage system, in reducing costs associated with repeated slide preparations, reducing turn-around time of pathology reports, in collaborative research and in teaching has been well documented. This paper highlights the experiences, gains and challenges encountered in the deployment of telepathology in two resource-constrained settings in Nigeria. Overcoming the challenges associated with setting up a telepathology service in sub-Saharan Africa is important as it has the potential to improve overall health outcomes in a medically underserved region while ensuring technology and knowledge transfer are achieved.