Reviewerly/exHarmony huggingface dataset:
Information extraction for different layouts of invoice images

In the organization, they purchased goods or services from different suppliers and used invoice documents to confirm the payment. The invoice documents contained information that can be used for a business decision but, the process of information extraction required many resources to collect the data. The traditional way used template matching-based methods. This process identifies the parts on an image that match a predefined template and requires new manual annotation when processing the new image layout. Therefore, developing a system for robustly extracting entities from different layouts of invoices is necessary. Existing research applied deep learning and Name Entity Recognition (NER) for information extraction but, extracting invoice information was widely done in English and Chinese languages. In this study, we constructed a deep learning model using BiLSTM-CRF (Bidirectional Long Short-Term Memory-Conditional Random Fields) with word and character embedding for information extraction from different layouts of Thai invoice images. The model was evaluated by Semantic Evaluation at a full named-entity level. Our experimental results showed that this method can achieve a precision of 0.9557, recall of 0.9486, and F1-score of 0.9521 for the partial match; precision of 0.9329, recall of 0.9259, and F1-score of 0.9294 for the exact match and the result of the F1-score was significantly influenced by the quality of images and text result from Optical Character Recognition (OCR).Abbreviations: BERT: bidirectional encoder representations from transformers; BiLSTM: bidirectional long short-term memory; COR: correct; CRF: conditional random fields; CV: computer vision; ELMO: embeddings from language model; INC: incorrect; MIS: missing; MSE: mean squared error; MUC: message understanding conference; NER: named entity recognition; NLP: natural language processing; OCR: optical character recognition; PAR: partial; SemEval: semantic evaluation; SPU: spurius
Multi-View Human Action Recognition Using Skeleton Based-FineKNN with Extraneous Frame Scrapping Technique

Human action recognition (HAR) is one of the most active research topics in the field of computer vision. Even though this area is well-researched, HAR algorithms such as 3D Convolution Neural Networks (CNN), Two-stream Networks, and CNN-LSTM (Long Short-Term Memory) suffer from highly complex models. These algorithms involve a huge number of weights adjustments during the training phase, and as a consequence, require high-end configuration machines for real-time HAR applications. Therefore, this paper presents an extraneous frame scrapping technique that employs 2D skeleton features with a Fine-KNN classifier-based HAR system to overcome the dimensionality problems.To illustrate the efficacy of our proposed method, two contemporary datasets i.e., Multi-Camera Action Dataset (MCAD) and INRIA Xmas Motion Acquisition Sequences (IXMAS) dataset was used in experiment. We used the OpenPose technique to extract the 2D information, The proposed method was compared with CNN-LSTM, and other State of the art methods. Results obtained confirm the potential of our technique. The proposed OpenPose-FineKNN with Extraneous Frame Scrapping Technique achieved an accuracy of 89.75% on MCAD dataset and 90.97% on IXMAS dataset better than existing technique.
From transformation to normalisation: An exploratory study of a large-scale agile transformation

Following the highly pervasive and effective use of agile methods at the team level, many software organisations now wish to replicate this success at the organisational level, adopting large-scale agile methods. However, an analysis of extant literature reveals (i) a lack of theoretically grounded studies of large-scale agile transformations; (ii) weak assumptions underpinning those that do exist; and (iii) an overly simplistic and often problematic over-reliance on simple adherence to agile methods. In addition, there is a lack of insights on why large-scale agile transformations fail. To explore this, we apply normalisation process theory (NPT) to examine key challenges in normalising (i.e. the process of embedding and sustaining) large-scale agile methods. We present an in-depth case study of large-scale agile transformation and demonstrate how failing to normalise a large-scale agile method can unravel an organisation’s transformation efforts. There are four key contributions from this research. First, this study shows that the standard focus on initial adoption is one of the main reasons for the widespread failure of digital transformations and in this case a large-scale agile transformation. Second, this is the first study to introduce and empirically apply NPT to information system development (ISD) as an illustrative guide for those across information systems field to follow. Third, we present key recommendations based on NPT to guide researchers and practitioners on the normalisation of large-scale transformations. Fourth, we present an agenda for future research on large-scale agile transformations.
The construct of cuteness: A validity study for measuring content and evoked emotions on social media

Social media users are often exposed to cute content that evokes emotional reactions and influences them to feel or behave certain ways. The cuteness phenomenon in social media has been scarcely studied despite its prevalence and potential to spread quickly and affect large audiences. The main framework for understanding cuteness and emotions related to cuteness outside of social media is baby schema (having juvenile characteristics), which triggers parental instincts. We propose that baby schema is a necessary but not sufficient component of explaining what constitutes cuteness and how people react to it in the social media context. Cute social media content may also have characteristics that evoke approach motivations (a desire to interact with an entity, generally with the expectation of having a positive experience) that can manifest behaviorally in sharing and other prosocial online behaviors. We developed and performed initial validation for measures in social media contexts of: (1) cute attributes that encompass both baby schema and other proposed cuteness characteristics (the Cuteness Attributes Taxonomy, CAT) and (2) the emotional reactions they trigger (Heartwarming Social Media, HSM). We used the Kama Muta Multiplex Scale (KAMMUS Two), as previously validated measure of kama muta (an emotion akin to tenderness; from Sanskrit, "moved by love") as a measure of emotional reaction to cute stimuli and the dimension Cute Content of the Social Media Emotions Annotation Guide (SMEmo-Cute Content) as a developed measure of gestalt cute content to help validate our newly developed measures. Using 1,875 Polish tweets, our results confirmed that cute social media content predicted a kama muta response, but not all KAMMUS Two subscales were sensitive to cute content, and that the HSM measure was a better indicator of the presence of cute content. Further, the CAT measure is an effective means of categorizing cute attributes of social media content. These results suggest potential differences between in-person, online, and social media experiences evoking cute emotional reactions, and the need for metrics that are developed and validated for use in social media contexts.
Chaos theory in metaheuristics

Metaheuristic optimization is the technique of finding the most suitable solution among the possible solutions for a particular problem. We encounter many problems in the real world, such as timetabling, path planning, packing, traveling salesman, trajectory optimization, and engineering design problems. The two main problems faced by all metaheuristic algorithms are being stuck in local optima and early convergence. To overcome these problems and achieve better performance, chaos theory is included in the metaheuristic optimization. The chaotic maps are employed to balance the exploration and exploitation efficiently and improve the performance of algorithms in terms of both local optima avoidance and convergence speed. The literature shows that chaotic maps can significantly boost the performance of metaheuristic optimization algorithms. In this chapter, chaos theory and chaotic maps are briefly explained. The use of chaotic maps in metaheuristic is presented, and an enhanced version of GSA with chaotic maps is shown as an application.
Metaheuristics for clustering problems

Data clustering is an essential, unsupervised classification method for discovering hidden patterns or information of a dataset. The literature shows metaheuristics are a reliable alternative to conventional clustering algorithms for data clustering problems. In this chapter, we compare 12 metaheuristics in this area. The algorithms used are Genetic Algorithm (GA), Gray Wolf Optimizer (GWO), Differential Evolution (DE), Biogeography-based Optimization (BBO), Harmony Search (HS), Particle Swarm Optimization (PSO), African Vulture Optimization Algorithm (AVOA), Firefly Algorithm (FFA), Symbiotic Organism Search (SOS), Artificial Bee Colony (ABC) algorithm, Whale Optimization Algorithm (WOA), and Artificial Gorilla Troops Optimization (AGTO). These algorithms were selected based on their unique capabilities. To test and evaluate the performance of the selected algorithms to solve the clustering problem, we use 10 standard datasets in the UCI repository. In addition, we use the ANOVA statistical test to examine the significant differences in the solutions obtained from the compared optimization algorithms.
A Comprehensive Study on Ethereum Blockchain-based Digital Marketplace using NFT Smart Contract Infrastructure

Every day thousands or millions of digital content or assets have been created and transected through the digital marketplace. Most of the cases after creating the content in the digital market a seller completely lose their content ownership and creative content. Not only that the entire system is centralized which means the whole transaction, information sharing, and database update are maintained by the third-party organization. And no contact verification and authentication happen between two parties. Also, generate the possibilities of data manipulation risk for a large amount of data. To solve this problem, we have introduced Ethereum blockchain-based digital marketplace where we use NFT-based smart contracts to verify and authenticate every transaction between buyer, seller, and marketplace owner. Our proposed system operated as a decentralized and distributed ledger. To develop the NFT-based smart contract we use the ERC721 token standard which provides us the non-fungible properties. We use two smart contracts to develop the whole infrastructure. One is for creating a market item and another one is for the marketplace. Also, develop the payment mechanism for our proposed structure and collect the listing fees. So, the proposed Ethereum blockchain-based digital marketplace is secure, trusted, immutable, and properly authenticated by the decentralized network.
Real time traffic sign detection and recognition for autonomous vehicle

The advancement of technology has made it possible for modern cars to utilize an increasing number of processing systems. Many methods have been developed recently to detect traffic signs using image processing algorithms. This study deals with an experiment to build a CNN model which can classify traffic signs in real-time effectively using OpenCV. The experimentation method involves building a CNN model based on a modified LeNet architecture with four convolutional layers, two max-pooling layers and two dense layers. The model is trained and tested with the German Traffic Sign Recognition Benchmark (GTSRB) dataset. Parameter tuning with different combinations of learning rate and epochs is done to improve the model’s performance. Later, this model is used to classify the images introduced to the camera in real- time. The graphs depicting the accuracy and loss of the model before and after parameter tuning are presented. Also, an experiment was performed to classify the traffic sign image introduced to the camera by using the CNN model. A high probability score is achieved during the process.
Creating CREATE queries with multi-task deep neural networks

Text-to-SQL is the task of mapping natural language utterances to structured query language (SQL). Prior studies focus on information retrieval aspect of this task. In this paper, we demonstrate a new use case for the text-to-SQL studies where a user can create database models from natural language and introduce the first dataset for this task. Furthermore, we propose a framework that consists of three modular components: (1) classifier component which predicts the data type and constraints of a column, (2) constraint component which establishes foreign key relationships between tables, (3) query component which generates a series of CREATE queries through a slot-filling approach. We propose various baseline models to evaluate the classifier component in different aspects. Each model is based on a state-of-the-art pre-trained language model that allows us to assess contextualized word representations in the table creation task. The obtained results showed that such representations play a vital role in classifying column data types and constraints correctly. One of the downsides of pre-trained models is the training time and the model size. Our experiments revealed that a multi-task BERT model achieving 75% and 96% accuracy for the data type and constraint prediction tasks, respectively, effectively addresses both problems.
More efficient tightly-secure lattice-based IBE with equality test

Identity-based encryption with equality test (IBEET) combines public key encryption with equality test (PKEET) and identity-based encryption (IBE). In an IBEET scheme, a user with trapdoors is allowed to verify whether different ciphertexts encrypted under different identities correspond to equal plaintexts through the equality test algorithm. Recently, several IBEET schemes were constructed over lattices to resist the potential quantum threats, perhaps since the latticed-based candidates are the most favored ones in the current NIST PQC standardization. However, almost all of the schemes are developed from Lee et al.’s generic construction and hence double both public parameters and ciphertext sizes. In this paper, we propose a more efficient and tightly-secure IBEET scheme which not only achieves a tight reduction with the semi-adaptive security, but also reduces both public parameters and ciphertext sizes.
Cost-sensitive max-margin feature selection for SVM using alternated sorting method genetic algorithm

This article introduces Alternated Sorting Method Genetic Algorithm (ASMGA), a simultaneous feature selection and model selection algorithm for Support Vector Machine (SVM) classifiers. It is a hybrid wrapper-filter algorithm that combines Genetic Algorithm (GA) with Max-Margin Feature Selection (MMFS). MMFS is a filter model that estimates feature importance based on feature relevance and redundancy. In this research, the idea of different error costs was used to introduce cost sensitivity to ASMGA. Thus, ASMGA selects relevant and independent features in a cost-sensitive manner. This research investigates the relationship between the cost sensitivity of SVM models produced by ASMGA and the feature selection process. ASMGA approximates a set of Pareto optimal feature subsets based on three objectives: cost-sensitive error rate, feature subset size, and MMFS-based estimates of feature relevance and redundancy. This research introduces a technique for handling multiple objectives. During the search, ASMGA alternates between two multi-objective sorting techniques: Weighted Sum (WS) of objectives and Non-dominated Sorting (NDS), according to a schedule of methods. This technique allows ASMGA to work as elitist GA for some iterations and as a Non-dominated Sorting Genetic Algorithm (NSGA-II) for the remaining iterations. The proposed algorithm was tested on 11 benchmark datasets and compared to canonical GA and NSGA-II. The algorithm and its variations performed on average 3.8% better than GA and NSGA-II for balanced datasets and 6.6% better for imbalanced datasets. The results and analysis in this article showcase the potential of ASMGA and help explain the interaction between cost sensitivity and feature selection.
Contact tracing apps, nationalism, and users with disability in the Global South: The faith in state and collective objective

Contact tracing apps have magnified the potential usefulness of mobile media and communication technologies for responding to disruptive events such as the COVID-19 pandemic. Their efficacy and ethical debates have become the focus of recent studies in the Global North. Reasons for using contact tracing apps among users with disability living in the Global South, however, seem to be understudied. Through cases from Indonesia and Vietnam, this study found that nationalistic values were among the reasons for using contact tracing apps as reflected in the users’ faith in the state and inclination to support its collective objective to control the pandemic. The users believed the state would be accountable in managing the personal and mobility data the contact tracing apps collected. Using contact tracing apps represented the users’ sense of capabilities to individually partake in the existing efforts to control the pandemic.
Toward the internet of things forensics: A data analytics perspective

Abstract The widespread use of networked, intelligent, and adaptable devices in various domains, such as smart cities and home automation, climate control, manufacturing and logistics, healthcare, education, and agriculture, has been hastened by recent developments in hardware and software technologies. In all these application domains, the concept of the Internet of Things helps to achieve process automation and decrease labor costs. While IoT has been an established domain for quite a while, it has seen a lot of advances and challenges in different subdomains over the years. One such subdomain is IoT Forensics which involves digital forensics concerning IoT devices, networks, or clouds. In this process of obtaining substantial evidence from the devices, networks, or cloud, a large amount of data and operations on said data are involved. Hence, looking through IoT Forensics through the methodology dealing with data, known as data analytics, is essential. This paper presents an interpretation of IoT Forensics from the standpoint of data analytics. To explain the same in detail, the paper focuses on IoT Forensics, its methodologies, and how they relate to data analytics stages. Toward the end, the paper discusses current developments in IoT Forensics from the data analytics perspective, limitations observed in the existing technologies, adoption challenges, and possible future advancements.
Hedge and safe-haven properties of Cryptocurrencies: evidence in east asia-5 market

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When black box algorithms are (not) appropriate

In the 1980s a new, extraordinarily productive way of reasoning about algorithms emerged. In this paper, we introduce the term “outcome reasoning” to refer to this form of reasoning. Though outcome reasoning has come to dominate areas of data science, it has been under-discussed and its impact under-appreciated. For example, outcome reasoning is the primary way we reason about whether “black box” algorithms are performing well. In this paper we analyze outcome reasoning’s most common form (i.e., as “the common task framework”) and its limitations. We discuss why a large class of prediction-problems are inappropriate for outcome reasoning. As an example, we find the common task framework does not provide a foundation for the deployment of an algorithm in a real world situation. Building off of its core features, we identify a class of problems where this new form of reasoning can be used in deployment. We purposefully develop a novel framework so both technical and non-technical people can discuss and identify key features of their prediction problem and whether or not it is suitable for outcome reasoning.
Global attention retinex network for low light image enhancement

Most low-light image enhancement methods only adjust the brightness, contrast and noise reduction of low-light images, making it difficult to recover the lost information in darker areas of the image, and even cause color distortion and blurring. To solve the above problems, a global attention-based Retinex network (GARN) for low-light image enhancement is proposed in this paper. We propose a novel global attention module which computes multiple dimensional information in the channel attention module to help facilitate inference learning. Then the global attention module is embedded into different layers of the network to extract richer shallow texture features and deep semantic features. This means that the rich features are more conducive to learning the mapping relationship between low-light images to normal-light images, so that the detail recovery of dark regions is enhanced in low-light images. We also collected a low/normal light image dataset with multiple scenes, in which the images paired as training set can succeed to be applied to low-light image enhancement under different lighting conditions. Experimental results on publicly available datasets show that our method has better effectiveness and generality than the state-of-the-art methods in terms of evaluations metrics such as PSNR, SSIM, NIQE, Entropy.
Intensity inhomogeneity image segmentation based on the gradient-based spaces and the prior constraint

Image segmentation is a fundamental task in computer vision and image processing. How to efficiently decrease the effect such as high noise, low resolution and intensity inhomogeneity is the key to improve the accuracy of segmentation. To this end, we propose a novel segmentation model by assuming the image to be the product of a piecewise-constant function and a smooth function and then the model can be proposed based on the suitable gradient spaces. To improve the robustness of the proposed model, we add a prior constraint for the smooth function and then analyse the existence of the solution based on the functional theory. Since the proposed model includes two nonsmooth terms, we use a heat kernel convolution to approximately replace the length regularization term and then the alternating direction method of multipliers can be employed to solve it. Numerical experiments on several testing images demonstrate that the effectiveness and the robustness of the proposed model compared to some state-of-the-art segmentation models.