Reviewerly/exHarmony huggingface dataset:
Foresight and strategic decision-making framework from artificial intelligence technology development to utilization activities in small-and-medium-sized enterprises

Purpose This study aims to predict artificial intelligence (AI) technology development and the impact of AI utilization activity on companies, to identify AI strategies dealing with the broad innovation activity of AI, and to construct the strategic decision-making framework of AI strategies for a small- and medium-sized enterprise (hereafter SME), to improve strategic decision-making practices of AI strategy in SMEs. Design/methodology/approach This study used the multiple methods on the design of two data collection stages. The first stage is an expertise-based approach. It organized the three groups of expert panels and conducted the Delphi survey on them in combination with the brainstorming of technology, innovation and strategy in the fourth industrial revolution. The second stage is in the complement approach of expertise-based results. It used the literature review to involve the analysis of academic and practical papers, reports and audio materials relating to technology development, innovation types and strategies of AI. Additionally, it organized the four semi-structured interviews. Finally, this study used the mind-map and decision tree to conduct each analysis and synthesize each analytical result. Findings This study identifies the precondition and four paths of AI technological development classifying into specialized AI, AI convergence with other technologies, general AI and AI control methods. It captures the impact of non- and technological innovation through AI on companies. Second, it identifies and classifies the six types of AI strategy: the bystander, capability-building, capability-holding, management-enhancing, market-enhancing and new-market-creating strategy. By using the decision tree, it constructs the strategic decision-making framework containing six AI strategies. Actionable points, strategic priorities and relevant instruments are suggested. Research limitations/implications The strategic decision-making framework covering from AI technology development to utilization in a SME can help understand the strategic behaviours in SMEs. The typology of six AI strategies implies the broad innovation behaviours in SMEs. It can lead to further research to understand the pattern of strategic and innovation behaviour on AI. Practical implications This practical study can help executives, managers and engineers in SMEs to develop their strategic practices through the strategic decision framework and six AI strategies. Originality/value This practical study elicits the six types of AI strategy and constructs the strategic decision-making framework of six AI strategies from AI technology development to utilization. It can contribute to improving the practices of strategic decision-making in SMEs.
<scp>Evonne</scp>: A Visual Tool for Explaining Reasoning with OWL Ontologies and Supporting Interactive Debugging

Abstract OWL is a powerful language to formalize terminologies in an ontology. Its main strength lies in its foundation on description logics, allowing systems to automatically deduce implicit information through logical reasoning. However, since ontologies are often complex, understanding the outcome of the reasoning process is not always straightforward. Unlike already existing tools for exploring ontologies, our visualization tool Evonne is tailored towards explaining logical consequences. In addition, it supports the debugging of unwanted consequences and allows for an interactive comparison of the impact of removing statements from the ontology. Our visual approach combines (1) specialized views for the explanation of logical consequences and the structure of the ontology, (2) employing multiple layout modes for iteratively exploring explanations, (3) detailed explanations of specific reasoning steps, (4) cross‐view highlighting and colour coding of the visualization components, (5) features for dealing with visual complexity and (6) comparison and exploration of possible fixes to the ontology. We evaluated Evonne in a qualitative study with 16 experts in logics, and their positive feedback confirms the value of our concepts for explaining reasoning and debugging ontologies.
DA-FPN: Deformable Convolution and Feature Alignment for Object Detection

This study sought to address the problem of the insufficient extraction of shallow object information and boundary information when using traditional FPN structures in current object detection algorithms, which degrades object detection accuracy. In this paper, a new FPN structure model, DA-FPN, is proposed. DA-FPN replaces the 1 × 1 convolution used in the conventional FPN structure for lateral connection with a 3 × 3 deformable convolution and adds a feature alignment module after the 2x downsampling operation used for lateral connection. This design allows the detection framework to extract more accurate information about the boundary of the object, particularly the boundary information of small objects. A bottom-up module was also added to incorporate the shallow information of the object more accurately into the high-level feature map, and a feature alignment module was added to the bottom-up module, thereby improving object detection accuracy. The experimental results show that DA-FPN can improve the accuracy of the single-stage object detection algorithms FoveaBox and GFL by 1.7% and 2.4%, respectively, on the MS-COCO dataset. This model was also found to improve the two-stage object detection algorithm SABL by 2.4% and offer higher small object detection accuracy and better robustness.
Algorithm for Generating S-Boxes with Prescribed Differential Properties

Cryptographic S-boxes are vectorial Boolean functions that must fulfill strict criteria to provide security for cryptographic algorithms. There are several existing methods for generating strong cryptographic S-boxes, including stochastic search algorithms. These search algorithms typically generate random candidate Boolean functions (or permutations) that are improved during the search by examining the search space in a specific way. Here, we introduce a new type of stochastic algorithm for generating cryptographic S-boxes. We do not generate and then improve the Boolean function; instead, we build the vector of values incrementally. New values are obtained by randomized search driven by restrictions on the differential spectrum of the generated S-box. In this article, we formulate two new algorithms based on this new approach and study the better one in greater detail. We prove the correctness of the proposed algorithm and evaluate its complexity. The final part contains an experimental evaluation of the method. We show that the algorithm generates S-boxes with better properties than a random search. We believe that our approach can be extended in the future by adopting more advanced stochastic search methods.
Deep Learning of Phase-Contrast Images of Cancer Stem Cells Using a Selected Dataset of High Accuracy Value Using Conditional Generative Adversarial Networks

Artificial intelligence (AI) technology for image recognition has the potential to identify cancer stem cells (CSCs) in cultures and tissues. CSCs play an important role in the development and relapse of tumors. Although the characteristics of CSCs have been extensively studied, their morphological features remain elusive. The attempt to obtain an AI model identifying CSCs in culture showed the importance of images from spatially and temporally grown cultures of CSCs for deep learning to improve accuracy, but was insufficient. This study aimed to identify a process that is significantly efficient in increasing the accuracy values of the AI model output for predicting CSCs from phase-contrast images. An AI model of conditional generative adversarial network (CGAN) image translation for CSC identification predicted CSCs with various accuracy levels, and convolutional neural network classification of CSC phase-contrast images showed variation in the images. The accuracy of the AI model of CGAN image translation was increased by the AI model built by deep learning of selected CSC images with high accuracy previously calculated by another AI model. The workflow of building an AI model based on CGAN image translation could be useful for the AI prediction of CSCs.
Potential of the Crypto Economy in Financial Management and Fundraising for Tourism

This study aims to examine the potential of blockchain technology in the financing and financial management of entrepreneurial tourism projects. It highlights two objectives: how the technology can be used as an alternative financing tool and how it can improve efficiency and transparency in the financial management of tourism companies. This study shows that initial coin offerings are an effective way to finance innovative tourism projects and that blockchain technology can improve the competitiveness and efficiency of tourism companies. Due to the lack of empirical data on the actual implementation and impact of blockchain technology in the tourism industry, it is suggested that further research is needed to examine the practical application of blockchain technology in the tourism industry, its potential impact on tourism businesses and its implications for the regulatory framework. The proposed methodology includes a systematic literature review on the application of blockchain technology for the financing of tourism projects and the financial improvement of tourism business models. The results indicate that blockchain technology has the potential to transform the financing and financial management of the tourism industry and improve its efficiency and transparency. Furthermore, combining blockchain with other technologies can provide additional benefits in supply chain management and event automation.
Causal reasoning for algorithmic fairness in voice controlled cyber-physical systems

Automated speaker recognition is enabling personalized interactions with the voice-based interfaces and assistants part of the modern cyber-physical-social systems. Prior studies have unfortunately uncovered disparate impacts across demographic groups on the outcomes of speaker recognition systems and consequently proposed a range of countermeasures. Understanding why a speaker recognition system may lead to this disparate performance for different (groups of) individuals, going beyond mere data imbalance reasons and black-box countermeasures, is an essential yet under-explored perspective. In this paper, we propose an explanatory framework that aims to provide a better understanding of how speaker recognition models perform as the underlying voice characteristics on which they are tested change. With our framework, we evaluate two state-of-the-art speaker recognition models, comparing their fairness in terms of security, through a systematic analysis of the impact of more than twenty voice characteristics. Our findings include important takeaways to enable voice controlled cyber-physical-social systems for everyone. Source code and data are available at https://bit.ly/EA-PRLETTERS.
A text classification approach to API type resolution for incomplete code snippets

The Stack Overflow Q&A platform boasts an active community of users who often include code snippets in their questions and answers. Several development tools rely on these code snippets as a source of information. Although code snippets are intended as examples for humans, they may not form compilation units. For instance, snippets illustrating how to use an API might lack the import statements for the corresponding API types. Thus, it becomes essential to determine the fully-qualified name of API types in incomplete snippets. We present RESICO, a machine learning-based text classification approach to resolving the simple name of API types to their fully-qualified names. RESICO is trained on a corpus of Java programs for which a compiler can determine the fully-qualified names. For four machine learning classifiers, we evaluate the type resolution accuracy of the resulting models on the original and an extended version of datasets of snippets previously used to evaluate the current state-of-the-art approach based on information retrieval. Results show that our approach outperforms the state-of-the-art one, although the training phase is slightly slower. We observe that most of the incorrect type resolutions are not due to ambiguities among the simple names for API types but due to similarities among the contexts in which these types are used, representing a future research challenge.
Depthwise Temporal Non-Local Network for Faster and Better Dynamic Hand Gesture Authentication

Dynamic hand gesture is an emerging and promising biometric trait. It contains both physiological and behavioral characteristics, which on the one hand can theoretically make authentication systems more accurate and more secure, and on the other hand can increase the difficulty of model design because it is essentially a fine-grained video understanding task. For authentication systems, equal error rate (EER) and real-time performance are two vital metrics. Current video understanding-based hand gesture authentication methods mainly focus on lowering the EER while neglecting to reduce the computational cost. In this paper, we propose a 2D CNN-based depthwise temporal non-local network (DwTNL-Net) that can take into account both EER and running efficiency. To enable the DwTNL-Net with spatiotemporal information processing capability, we design a temporal sharpening (TS) module and a DwTNL module for short- and long-term identity feature modeling, respectively. The TS module can assist the backbone in local behavioral characteristic understanding and can simultaneously remove redundant information and highlight behavioral cues while retaining sufficient physiological characteristics. In contrast, the DwTNL module focuses on summarizing global information and discovering stable patterns, which are finally used for local information enhancement. The complementary combination of our TS and DwTNL modules makes DwTNL-Net achieve substantial performance improvements. Extensive experiments on the SCUT-DHGA dataset and sufficient statistical analyses fully demonstrate the superiority and efficiency of our DwTNL-Net. The code is available at https://github.com/SCUT-BIP-Lab/DwTNL-Net.
Remote Sensing Image Recovery and Enhancement by Joint Blind Denoising and Dehazing

Due to the hazy weather and the long-distance imaging path, the captured remote sensing image (RSI) may suffer from detail loss and noise pollution.However, simply applying dehazing operation on a noisy hazy image may result in noise amplification.Therefore, in this paper, we propose joint blind denoising and dehazing for RSI recovery and enhancement to address this problem.Firstly, we propose an efficient and effective noise level estimation method based on quad-tree subdivision and integrate it into FFDNet for blind denoising.Secondly, a multi-scale guided filter decomposes the denoised hazy image into base and detailed layers, separating the initial details.Then, the dehazing procedure using the corrected boundary constraint is implemented in the base layer, while a nonlinear sigmoid mapping function enhances the detailed layers.The last step is to fuse the enhanced detailed layers and the dehazed base layer to get the final result.Using both synthetic remote sensing hazy image (RSHI) datasets and real-world RSHI, we perform comprehensive experiments to evaluate the proposed method.Results show that our method is superior to well-known methods in both dehazing and joint denoising and dehazing tasks.
LEAP: Lightweight and Efficient Accelerator for Sparse Polynomial Multiplication of HQC

The Hamming quasi-cyclic (HQC) code-based encryption scheme is one of the fourth-round algorithms selected by the National Institute of Standards and Technology (NIST) postquantum cryptography (PQC) standardization process. However, very few hardware implementations have been reported for HQC to date. In this brief, we propose a novel Lightweight and Efficient Accelerator for sparse Polynomial multiplication (LEAP) of HQC, compatible with different parameters, on the field-programmable gate array (FPGA) platform. First, we give a mathematical derivation process for the sparse polynomial multiplication deployed in HQC. Then, we explain the proposed hardware structure in detail. Finally, we present the FPGA implementation results to confirm the efficiency of the proposed LEAP, for example, the proposed design for hqc-192 has at least 31.03% less area-delay product (ADP) than the existing design. LEAP can be extended further to construct efficient HQC cryptoprocessors.
Real-World Non-Homogeneous Haze Removal by Sliding Self-Attention Wavelet Network

In complex natural haze scenes, image haze removal still faces significant challenges in removing non-homogeneous and dense haze. The double complexity of haze distribution, on the one hand, is reflected in the interference of haze to the global image information, and on the other hand, it is reflected in the imbalance of image brightness and color caused by random haze distribution. In natural scenes with prominent edge and texture features, the above problems may cause severe degradation of image quality and performance of various tasks. Numerous studies on network learning show that the effect of haze removal is closely related to haze feature expression. Therefore, to improve the performance of dehazing, this paper proposes a sliding self-attention wavelet network. Specifically, we first design a sliding self-attention module to identify haze regions in images and capture rich haze-related feature information. Then, considering the uneven distribution of haze in images, discrete wavelet transform (DWT) and inverse transform (IDWT) are used for constructing a hierarchical encoder-decoder structure, which can fully use the multi-resolution characteristics of DWT, locally decompose feature maps of different scales, extract low and high-frequency information, and then gradually recover sharp edges and precise texture details from hazy images. Finally, to enable the proposed network to generate more realistic haze-free images on different complex haze scenes, we develop a DWT-based adversarial loss function to constrain the low and high-frequency components of generated images closer to the corresponding clear images. Experimental results on the relevant public benchmark datasets show that the proposed algorithm achieves favorable dehazing performance.
An Improved Image Denoising Technique by designing Cascaded Filter using Slime Mould Algorithm

This paper presents an improved image-denoising technique that is based on the combination of cascaded filters. The amalgamation of different filters is acquired by using the Slime Mould Algorithm (SMA) and cascading four filters among twelve filters which provide improved denoising performance. SMA has a new and individual mathematical model and this model employs adaptive measures to stimulate the procedure of generating positive and negative feedback of the propagation wave of Slime Mould. The entire model is supported by a bio-oscillator that can build the optical path for collecting food with excellent exploitation propensity and exploratory potentiality. Different denoising techniques are suitable for either Salt & pepper, Gaussian, or Speckle noise but our proposed algorithm helps in removing all three noises. Denoised images obtained by the proposed denoising technique are far superior to denoised images obtained by other denoising techniques in the case of visual and quantitative analysis. The proposed algorithm also provides a significant improvement compared to other algorithms.
Performance Evaluation of Text Augmentation Methods with BERT on Small-sized, Imbalanced Datasets

Recently deep learning methods have achieved great success in understanding and analyzing text messages. In real-world applications, however, labeled text data are often small-sized and imbalanced in classes due to the high cost of data collection and human annotation, limiting the performance of deep learning classifiers. Therefore, this study explores an understudied area—how sample sizes and imbalance ratios influence the performance of deep learning models and augmentation methods—and provides a solution to this problem. Specifically, this study examines the performance of BERT, Word2Vec, and WordNet augmentation methods with BERT fine-tuning on datasets of sizes 500, 1,000, and 2,000 and imbalance ratios of 4:1 and 9:1. Experimental results show that BERT augmentation improves the performance of BERT in detecting the minority class, and the improvement is most significantly (15.6–40.4% F1 increase compared to the base model and 2.8%–10.4% F1 increase compared to the model with the oversampling method) when the data size is small (e.g., 500 training documents) and highly imbalanced (e.g., 9:1). When the data size increases or the imbalance ratio decreases, the improvement generated by the BERT augmentation becomes smaller or insignificant. Moreover, BERT augmentation plus BERT fine-tuning achieves the best performance compared to other models and methods, demonstrating a promising solution for small-sized, highly imbalanced text classification tasks.
Extracting Relations Between Sectors

The term "sector" in professional business life is a vague concept since companies tend to identify themselves as operating in multiple sectors simultaneously. This ambiguity poses problems in recommending jobs to job seekers or finding suitable candidates for open positions. The latter holds significant importance when available candidates in a specific sector are also scarce; hence, finding candidates from similar sectors becomes crucial. This work focuses on discovering possible sector similarities through relational analysis. We employ several algorithms from the frequent pattern mining and collaborative filtering domains, namely negFIN, Alternating Least Squares, Bilateral Variational Autoencoder, and Collaborative Filtering based on Pearson’s Correlation, Kendall and Spearman’s Rank Correlation coefficients. The algorithms are compared on a real-world dataset supplied by a major recruitment company, Kariyer.net, from Turkey. The insights and methods gained through this work are expected to increase the efficiency and accuracy of various methods, such as recommending jobs or finding suitable candidates for open positions.
Mob-YOLO: A Lightweight UAV Object Detection Method

With the increasing use of Unmanned Aerial Vehicles (UAVs) in various fields, the coordinated execution of tasks by multiple UAVs has become an important development trend in the future. To avoid the collision of multiple UAVs with each other during flight and ensure flight safety, it is essential to be able to achieve high-precision, real-time airborne UAV object detection. In this work, a UAV object detection method called Mob-YOLO is proposed. Based on the high-performance model YOLOv4, MobileNetv2, a lightweight convolutional neural network, is used to replace the original YOLOv4 backbone CSPDarknet53 for model size reduction and computing operation simplification. Meanwhile, to solve the issue of poor accuracy for small UAV objects after network replacement, this work also designs a multi-scale feature extraction and fusion branch to expand the receptive field of the object detector by multi-scale feature fusion. The proposed method is evaluated using a self-built UAV dataset. The results demonstrate that Mob-YOLO can satisfy accurate real-time monitoring of UAV objects, and the model size is tiny, which can be used for deployment on airborne embedded processors.