Reviewerly/exHarmony huggingface dataset:
Adoption of blockchain technology in organizations: from morality, ethics and sustainability perspectives

Purpose The purpose of this study is to examine how the adoption of blockchain technology can improve organizational sustainability and what are the contributions of morality, ethics and governance in this scenario. Design/methodology/approach This study has used different literature and theories to build a successful theoretical model and then validated it using the partial least squares structural equation modeling approach. Various statistical modeling analyses have been performed to test the robustness of the proposed model, which is found to be effective and unique as it has a high explanatory power. Findings This study has found that adoption of blockchain technology can improve the sustainability performance of organizations. It has also highlighted that organizational sustainability will be further enhanced if the underlying algorithms of blockchain technology are transparent to make the applications moral and ethical. This study has further demonstrated that appropriate governance of blockchain technology can support the organizations to enhance their sustainability performance. Research limitations/implications This study demonstrates the importance of an organization’s governance over blockchain technology and its impact on sustainability. The study provides valuable input to practitioners and policy makers regarding blockchain technology and its governance models. Also, the study provides valuable recommendations to leaders and managers of organizations on how to successfully adopt blockchain technology. Originality/value This study presents a unique theoretical model to explain the influence of the adoption of blockchain technology and an organization’s sustainability. The study also adds value to the overall body of literature in the areas of ethics, governance and blockchain technology along with sustainability.
Bidirectional Certificateless Searchable Authenticated Encryption for Encrypted Email Application in IoT

Today, the Internet of Things (IoT) framework can be incorporated into many fields (i.e., industrial, medical, academic, enterprises, etc.) that require an encrypted email application to exchange information. This application has a huge number of emails generated through the inbox and outboxes of their profiles that can’t be maintained by the system users locally, so they use the email gateway server services to store that data. To ensure the security of user data while searching, a Certificateless Searchable Authenticated Encryption (CLSAE) scheme is suitable for the IoT environment. But, the maximum of CLSAE schemes used costly bilinear pairing and was proven secure in the Random Oracle Model (ROM). Later, the researcher introduced another CLSAE system that does not require pairing and ROM, but the searching operation was not flexible enough to reduce the real-time scenario. This paper presents the Bidirectional Certificateless Searchable Authenticated Encryption (BCLSAE) scheme in the Standard Model (SM), which includes individual cipher-keywords algorithms for both sender and receiver, bidirectional search operation, and multi-keyword search. Furthermore, the system provides privacy of keyword information in the trapdoor, which leads to resistance to Keyword Guessing Attacks (KGAs) (launched by both an outsider and an insider). Finally, we compare the BCLSAE with related schemes, which outperform them.
Blockchain implementation and shareholder value: a complex adaptive systems perspective

Purpose Blockchain is a distributed ledger technology that uses cryptography to ensure transmission and access security, which provides solutions to numerous challenges to complex supply networks. The purpose of this paper is to empirically test the impact of blockchain implementation on shareholder value varying from internal and external complexity from the complex adaptive systems (CASs) perspective. It further explores how business diversification, supply chain (SC) concentration and environmental complexity affect the relationship between blockchain implementation and shareholder value. Design/methodology/approach Based on 138 blockchain implementation announcements of listed companies on the Chinese A-share stock market, the authors use event study methodology to evaluate the impact of blockchain implementation on shareholder value. Findings The results show that blockchain implementation has a positive impact on shareholder value, and this impact will be moderated by business diversification, SC concentration and environmental complexity. In addition, environmental complexity exerts a moderating effect on SC concentration. In the post hoc analysis, the authors further explore the impact of blockchain implementation on long-term operational performance. Originality/value This is the first research empirically examining the effect of blockchain implementation on shareholder value varying from internal and external complexity from the CASs perspective. This paper provides evidence of the different effects of blockchain implementation on short- and long-term performance. It adds to the interdisciplinary research of information systems (IS) and operations management (OM).
LANDMARK: language-guided representation enhancement framework for scene graph generation

Scene graph generation (SGG) is a sophisticated task that suffers from both complex visual features and the long-tail problem. Recently, various unbiased strategies have been proposed by designing novel loss functions and data balancing strategies. Unfortunately, these unbiased methods fail to emphasize language priors in the feature refinement perspective. Inspired by the fact that predicates are highly correlated with semantics hidden in subject-object pair and global context, we propose LANDMARK (LANguage-guiDed representation enhanceMent frAmewoRK) that learns predicate-relevant representations from language-vision interactive patterns, global language context, and object-predicate correlation. Specifically, we first project object labels to three distinctive semantic embeddings for different representation learning. Then, Language Attention Module (LAM) and Experience Estimation Module (EEM) processes subject-object word embeddings to attention vector and predicate distribution, respectively. Language Context Module (LCM) encodes global context from each word embedding, which avoids isolated learning from local information. Finally, module outputs are used to update visual representations and the SGG model’s prediction. All language representations are purely generated from object categories so that no extra knowledge is needed. This framework is model-agnostic and consistently improves performance on existing SGG models. Besides, representation-level unbiased strategies endow LANDMARK with compatibility of other methods. Code is available at https://github.com/rafa-cxg/PySGG-cxg .
Nonlinearity of functions over finite fields

Abstract The nonlinearity and additive nonlinearity of a function are defined as the Hamming distances, respectively, to the set of all affine mappings and to the set of all mappings having nontrivial additive translators. On the basis of the revealed relation between the nonlinearities and the Fourier coefficients of the characters of a function, convenient formulas for nonlinearity evaluation for practically important classes of functions over an arbitrary finite field are found. In the case of a field of even characteristic, similar results were obtained for the additive nonlinearity in terms of the autocorrelation coefficients. The formulas obtained made it possible to present specific classes of functions with maximal possible and high nonlinearity and additive nonlinearity.
Visual Information Extraction in the Wild: Practical Dataset and End-to-End Solution

Visual information extraction (VIE), which aims to simultaneously perform OCR and information extraction in a unified framework, has drawn increasing attention due to its essential role in various applications like understanding receipts, goods, and traffic signs. However, as existing benchmark datasets for VIE mainly consist of document images without the adequate diversity of layout structures, background disturbs, and entity categories, they cannot fully reveal the challenges of real-world applications. In this paper, we propose a large-scale dataset consisting of camera images for VIE, which contains not only the larger variance of layout, backgrounds, and fonts but also much more types of entities. Besides, we propose a novel framework for end-to-end VIE that combines the stages of OCR and information extraction in an end-to-end learning fashion. Different from the previous end-to-end approaches that directly adopt OCR features as the input of an information extraction module, we propose to use contrastive learning to narrow the semantic gap caused by the difference between the tasks of OCR and information extraction. We evaluate the existing end-to-end methods for VIE on the proposed dataset and observe that the performance of these methods has a distinguishable drop from SROIE (a widely used English dataset) to our proposed dataset due to the larger variance of layout and entities. These results demonstrate our dataset is more practical for promoting advanced VIE algorithms. In addition, experiments demonstrate that the proposed VIE method consistently achieves the obvious performance gains on the proposed and SROIE datasets. The code and dataset will be available at https://github.com/jfkuang/CFAM .
Handwritten Text Generation with Character-Specific Encoding for Style Imitation

In this paper, we propose a novel method for handwritten text generation that uses a style encoder based on a vision transformer network that encodes handwriting style from reference images and allows the generator to imitate it. The encoder learns to disentangle style information from the content by learning to recognize who wrote the text, and the self-attention mechanism in the encoder allows us to produce character-specific encodings by using characters in the target sequence as queries. Our method can also generate handwritten text images in random styles by sampling random latent vectors instead of encoding style vectors from reference images. We demonstrate through experiments that our proposed method outperforms existing methods for handwritten text generation in terms of the quality of generated images and their fidelity with respect to the distribution of real images. Furthermore, it achieves significantly better performance at imitating handwriting styles defined by reference images. Our model generalizes well to unseen data and can generate handwritten images of words and character sequences as well as imitate handwriting styles not included in the training data.
Vision Conformer: Incorporating Convolutions into Vision Transformer Layers

Transformers are popular neural network models that use layers of self-attention and fully-connected nodes with embedded tokens. Vision Transformers (ViT) adapt transformers for image recognition tasks. In order to do this, the images are split into patches and used as tokens. One issue with ViT is the lack of inductive bias toward image structures. Because ViT was adapted for image data from language modeling, the network does not explicitly handle issues such as local translations, pixel information, and information loss in the structures and features shared by multiple patches. Conversely, Convolutional Neural Networks (CNN) incorporate this information. Thus, in this paper, we propose the use of convolutional layers within ViT. Specifically, we propose a model called a Vision Conformer (ViC) which replaces the Multi-Layer Perceptron (MLP) in a ViT layer with a CNN. In addition, to use the CNN, we proposed to reconstruct the image data after the self-attention in a reverse embedding layer. Through the evaluation, we demonstrate that the proposed convolutions help improve the classification ability of ViT.
MBEANN for Robotic Swarm Controller Design and the Behavior Analysis for Cooperative Transport

This study focuses on mutation-based evolving artificial neural network (MBEANN), a topology and weight evolving artificial neural network (TWEANN) algorithm. TWEANN optimizes both the connection weights and neural network structure. Primarily, MBEANN uses only mutations to evolve artificial neural networks. An individual in an MBEANN is designed to have a set of sub-networks called operons. Operons are expected to have functions during evolution because they do not recombine with other operons. In this study, we applied MBEANN to design a controller for a robotic swarm on cooperative transport, where the following canonical evolving artificial neural network (EANN) methods do not work well. For comparison with MBEANN, we used an EANN with a fixed network structure and neuroevolution of augmenting topologies (NEAT), which is a widely used TWEANN algorithm. We confirmed that the robot controller that evolved with the MBEANN outperformed the structure-fixed EANN and NEAT controllers. In addition, we investigated the behavior of the swarm robot obtained using the proposed method, in which we deactivated each operon to extract its function. The results show that operons could have their functions, and that several operons could strengthen one another’s functions.
Design Principles for Interactive and Reflective Journaling with AI

Designing for reflection and journaling have been prominent research areas in HCI and Interaction Design. However, designing for the experience of journaling that is supported by conversations with AI–Conversational Agent (CA)–to foster reflection seems to be a relatively unexplored area. Furthermore, while there are an abundant number of general guidelines and design principles for designing human-AI interactions, a set of guidelines for designing an interactive and reflective journaling experience with AI is lacking. This paper is a first attempt to address that need. We present the result of a qualitative user study on interactive and reflective journaling. We were interested in attending to our participants’ experiences and finding out their needs regarding the interactive journaling experience with CA. The user needs then were translated to design requirements and thereafter to themes or design principles. Some of our findings suggest that one of the important factors in journaling is the personal aesthetics of writing, by using carefully selected personal tools, specific materiality and interactions. Further, the flow of writing is considered sacred, hence it is almost like an untouchable, reflective ritualistic flow. Reflecting on the findings, we believe the outcome of this study can create opportunities for designing for human-AI interactions that are generative and reflective for activities that require such qualities, such as journaling or creativity.
Synchronized Colored Petri Net Based Multimodal Modeling and Real-Time Recognition of Conversational Spatial Deictic Gestures

Gestures are an important part of intelligent human-robot interactions. Co-speech gestures are a subclass of gestures that integrate speech and dialogs with synchronous combinations of various postures, haptics (touch), and motions such as head, hand, index finger or palm, and gaze. Deictic gestures are a subclass of co-speech gestures that provide Spatio-temporal reference to entities in the field-of-vision, by pointing at an individual entity or collection of entities and referring to them using pronouns in spoken phrases. Deictic gestures are important for human-robot interaction due to their property of attention seeking and providing a common frame-of-reference by object localization. In this research, we identify different subclasses of deictic gestures and extend the Synchronized Colored Petri net (SCP) model to recognize deictic gestures. The proposed extension integrates synchronized motions of head, hand, index-finger, palm, gaze (eye-motion tracking and focus) with pronoun reference in speech. An implementation using video-frame analysis and gesture-signatures representing meta-level attributes of SCP has been described. An algorithm has been presented. Performance analysis shows that the recall is approximately 85 percent for deictic gestures, and conversational head-gestures are separated from deictic gestures 95 percent of the time. Results show that mislabeling in deictic gestures occurs due to missing frames, feature points, undetectable motions, and the choice of thresholds during video analysis.
YOLOv5 for Road Events Based Video Summarization

In recent years, metropolitan cities have seen a rapid rise in vehicular traffic, thereby increasing the risk of accidents. Automatic detection of accidents will shorten the response time for timely treatment and justice for the victims. Accordingly, this paper utilizes YOLOv5 for road events (traffic accident) detection and also proposes an event-based video summarization technique for reducing massive surveillance data storage. With the enforcement of privacy laws, it is challenging to get publicly available real-world road surveillance videos for training computer vision models. To cope with the scarcity of real-world data, this paper utilizes synthetic road surveillance data to train a YOLOv5 model for detecting road events from surveillance videos. The synthetically trained model is then tested on real-world road traffic surveillance videos. For experiments, the synthetic dataset is divided into 60% training, 20% validation, and 20% test sets. The proposed approach detects accidents across video frames and generates video summaries centered around the accident for faster future visual data analytics. Experimental results prove the efficacy of the developed scheme to achieve a reduction of storage space and the duration of summarized videos by a range of 20–50% for test videos.
Cybersecurity effectiveness: The role of internal auditor certification, risk assessment and senior management

This study aims to analyze and examine the influence of internal auditor certification, risk assessment, and the role of senior management on the effectiveness of cybersecurity for internal auditors who have experience in cybersecurity and information technology. This research method is a quantitative method, data analysis uses structural equation modeling (SEM) with SmartPLS 3.0 software tools. The population of this study is internal auditors who have experience in cybersecurity and information technology. The sample for this study was 480 respondents who were determined by the snowball sampling method. The research data was obtained from an online questionnaire which was distributed via social media. The questionnaire was designed using a Likert scale of 1 to 5. The stages of data analysis were validity test, reliability test and significance test. The results of this study indicate that internal auditor certification has a positive effect on cybersecurity effectiveness, risk assessment has a positive effect on cybersecurity effectiveness, and the role of senior management has a positive effect on cybersecurity effectiveness.
3D Particle Position Determination And Correction At High Particle Densities

The method of Iterative Particle Reconstruction (IPR), introduced by Wieneke in 2013, constitutes a major step towards high-density 3D Lagrangian Particle Tracking. It reconstructs 3D particle positions from their projections onto several cameras. In the first part of this work, we present several approaches to enhance the original IPR working principle which, in combination, nearly triple the processable particle image densities, allowing complete and ghost-free reconstructions on a single snapshot from a four-camera system at up to 0.14 particles-per pixel (ppp). The updated method is proven to be fast, accurate and robust against image noise and other imaging artifacts. A central piece of the IPR functionality is a position optimization algorithm, using the difference of the local re-projected and original images (the residual images) as a cost function and displacing particles along its steepest gradient ('shaking' of the particles). The same approach is used within the Shake-The-Box (STB) Lagrangian Particle Tracking (LPT) scheme to correct predicted particle positions. The positional errors to be corrected during IPR-processing are typically in the sub-pixel range, however larger errors can occur at the prediction stage of STB. The second part of this manuscript quantifies the ability of the position optimization to successfully correct misplaced particles and proposes a method to further increase this range. Cost-function-gradient based methods require a certain overlap of the re-projected image with the original image for any given particle. Still, a misplacement of 2-3 pixels - depending on its direction relative to the camera positions - is shown to be reliably correctable. As seen by statistics from a DNS of a turbulent cylinder flow, such high accelerations (and therefore mispredictions) are rare at typical sampling rates. Therefore, most particle predictions can be successfully optimized to the correct position. In order to additionally handle rare events of large acceleration and misplacement, an iterative grid-search is applied specifically to particles not being optimally placed yet ('Variable Space step', VS). Such particles are identified using the local shape of the cost-function gradient. Using synthetic data, it is demonstrated that this method is able to correct even large prediction errors with high reliability. Applying a low temporal sampling on the test case of turbulent cylinder flow results in 20.8 px average particle shift. In this case, using noisy image data at a particle image density of 0.1 ppp, approx. 95.3% of the particle predictions can be corrected by pure position optimization, while around 99.5 % are achieved by additionally applying VS. Transferring this approach to experimental data could further improve tracking fidelity and would allow relaxing on the temporal resolution demands.
Compositional Nutrient Diagnosis (CND) Norms and Indices for Potato (Solanum tuberosum L.)

A survey was conducted in potato fields for collection of leaf samples to establish nutrient concentration yield data bank. The data bank was used for developing multivariate compositional nutrient diagnosis (CND) norms for assessing the nutritional status of selected centres of potato growing fields. The mean N, P and K concentrations were 2.09, 0.25 and 4.16 %, respectively. The mean Ca (1.11%) concentration was twice higher compared to Mg (0.63 %) concentration. The mean values of Zn, Cu, Fe, Mn and B were 43.69, 31.24, 986.71, 192.76 and 59.98 ppm, respectively. The CND norms for V N, V P and V K were 3.04, 0.94 and 3.73, respectively. The norm for Ca (V Ca =2.45) and Mg (V Mg =1.78) were much narrower compared to the absolute nutrient concentration. The norm for V Zn , V Cu , V Fe , V Mn and V B were -3.24, -3.60, -0.23, -1.98 and -2.89 respectively. The multivariate CND norms developed for ten nutrients proved to be an important tool for diagnosis of nutrient imbalance in potato. The nutrient indices developed indicated that Zn was the most common yield-limiting nutrient. The CND norms and the indices developed can be used for identifying the hidden hunger of various nutrients in potato for evolving nutrient management strategies.