Reviewerly/exHarmony huggingface dataset:
ICT adoption, innovation and financial development in a digital world: empirical analysis from Africa

As a response to a digital era that is seen as a core element of global digitalisation, financial development in many countries including those in the African continent experienced varying patterns. These have made some countries to be categorised as relatively high and others relatively low in terms of their scale on digital economy. Thus, this study empirically investigates the interaction of information and communication technology (ICT) adoption and innovation, and the role of this digitalisation interaction on financial development in Africa, and across the sub-regions. It utilises the Bayesian Vector Auto-Regressive (BVAR) modelling to simulate the impulse response function and variance decomposition across Africa. The study finds that ICT-innovation interaction shock positively drives financial development across all of 6 datasets. This implies that for multinational corporations (MNCs) and other economic agents, ICT – innovation interaction should be strongly applied across all sectors to drive financial development since all sectors require finances to improve performance. Thus, contributes to the empirical testing of the theoretical reflections of digitalisation and digital economy interaction in African countries.
Learning to Count in the Crowd from Limited Labeled Data

Recent crowd counting approaches have achieved excellent performance. However, they are essentially based on fully supervised paradigm and require large number of annotated samples. Obtaining annotations is an expensive and labour-intensive process. In this work, we focus on reducing the annotation efforts by learning to count in the crowd from limited number of labeled samples while leveraging a large pool of unlabeled data. Specifically, we propose a Gaussian Process-based iterative learning mechanism that involves estimation of pseudo-ground truth for the unlabeled data, which is then used as supervision for training the network. The proposed method is shown to be effective under the reduced data (semi-supervised) settings for several datasets like ShanghaiTech, UCF-QNRF, WorldExpo, UCSD, etc. Furthermore, we demonstrate that the proposed method can be leveraged to enable the network in learning to count from synthetic dataset while being able to generalize better to real-world datasets (synthetic-to-real transfer).
Performance Analysis of Blockchain Platforms: Empirical Evaluation of Hyperledger Fabric and Ethereum

Recent years have witnessed a sizeable growth of Blockchain applications in enterprises. Blockchain is transforming the traditional approach of storing and managing data in a single location into a decentralized ledger. Although many industries are keen on adopting Blockchain technology for enhanced transaction transparency, the performance of the current Blockchain platforms is still perplexing to stakeholders. Therefore, this research aims to conduct an empirical study to evaluate the performance of two prominent Blockchain platforms, Hyperledger Fabric and Ethereum. The performance evaluation is based on measuring four metrics including success rate, average latency, throughput, and resource consumption. The experimental results of executing 100 transactions show that Hyperledger Fabric generally surpasses Ethereum against the four performance metrics. The presented results in this research would assist practitioners in their decision-making process of adopting the ideal Blockchain platform into their IT systems based on application requirements.
Log-flock: A blockchain-enabled platform for digital asset valuation and risk assessment in E-commerce logistics financing

E-commerce retail sales have seen significant growth in the past decade with its proliferation into the consumer markets at unprecedented rates. This has led to the rise of logistics companies (LC) and the advent of Logistics financing as a tool used by them to attain business expansion. The current climate in e-commerce that is characterized by an upswing in sales requires the LCs to equip themselves with sufficient capital to elevate or modernize their operational capabilities. But, these small and medium-sized enterprises tend to overlook their business expansion opportunities due to the unfavorable financing environment created due to their: (i) limited tangible assets (ii) lack of clarity on operating capability, and (iii) lack of reliable information-sharing mechanism. To address these problems, this study aims to utilize the digital assets of the LC to aid logistics financing. This study introduces a logistics financing platform (Log-Flock), that leverages the abilities of the Internet of Things (IoT), cyber-physical system (CPS), and blockchain technologies to support the utilization of digital assets of the LC for logistics financing. The IoT and CPS are combined to support the generation of the digital assets whereas the core constructs of blockchain technology, such as the tokens and smart contracts, are used to (i) introduce incentivized rewarding mechanisms to motivate the engagement of users and, (ii) to perform the digital asset valuation and risk assessment in logistics financing. Some of the expected benefits from the study include: (i) This novel platform if implemented, would significantly reduce the time for financing. (ii) It communicates the operating capability of the LCs in the form of credit rating which is easily comprehensible for the financing institutions.
Backpropagated Gradient Representations for Anomaly Detection

Learning representations that clearly distinguish between normal and abnormal data is key to the success of anomaly detection. Most of existing anomaly detection algorithms use activation representations from forward propagation while not exploiting gradients from backpropagation to characterize data. Gradients capture model updates required to represent data. Anomalies require more drastic model updates to fully represent them compared to normal data. Hence, we propose the utilization of backpropagated gradients as representations to characterize model behavior on anomalies and, consequently, detect such anomalies. We show that the proposed method using gradient-based representations achieves state-of-the-art anomaly detection performance in benchmark image recognition datasets. Also, we highlight the computational efficiency and the simplicity of the proposed method in comparison with other state-of-the-art methods relying on adversarial networks or autoregressive models, which require at least 27 times more model parameters than the proposed method.
Imbalanced Continual Learning with Partitioning Reservoir Sampling

Continual learning from a sequential stream of data is a crucial challenge for machine learning research. Most studies have been conducted on this topic under the single-label classification setting along with an assumption of balanced label distribution. This work expands this research horizon towards multi-label classification. In doing so, we identify unanticipated adversity innately existent in many multi-label datasets, the long-tailed distribution. We jointly address the two independently solved problems, Catastropic Forgetting and the long-tailed label distribution by first empirically showing a new challenge of destructive forgetting of the minority concepts on the tail. Then, we curate two benchmark datasets, COCOseq and NUS-WIDEseq, that allow the study of both intra- and inter-task imbalances. Lastly, we propose a new sampling strategy for replay-based approach named Partitioning Reservoir Sampling (PRS), which allows the model to maintain a balanced knowledge of both head and tail classes. We publicly release the dataset and the code in our project page.
Security and blockchain convergence with Internet of Multimedia Things: Current trends, research challenges and future directions

The Internet of Multimedia Things (IoMT) orchestration enables the integration of systems, software, cloud, and smart sensors into a single platform. The IoMT deals with scalar as well as multimedia data. In these networks, sensor-embedded devices and their data face numerous challenges when it comes to security. In this paper, a comprehensive review of the existing literature for IoMT is presented in the context of security and blockchain. The latest literature on all three aspects of security, i.e., authentication, privacy, and trust is provided to explore the challenges experienced by multimedia data. The convergence of blockchain and IoMT along with multimedia-enabled blockchain platforms are discussed for emerging applications. To highlight the significance of this survey, large-scale commercial projects focused on security and blockchain for multimedia applications are reviewed. The shortcomings of these projects are explored and suggestions for further improvement are provided. Based on the aforementioned discussion, we present our own case study for healthcare industry: a theoretical framework having security and blockchain as key enablers. The case study reflects the importance of security and blockchain in multimedia applications of healthcare sector. Finally, we discuss the convergence of emerging technologies with security, blockchain and IoMT to visualize the future of tomorrow's applications.
Unitary Transformation of the Electronic Hamiltonian with an Exact Quadratic Truncation of the Baker-Campbell-Hausdorff Expansion

The application of current and near-term quantum hardware to the electronic structure problem is highly limited by qubit counts, coherence times, and gate fidelities. To address these restrictions within the variational quantum eigensolver (VQE) framework, many recent contributions have suggested dressing the electronic Hamiltonian to include a part of electron correlation, leaving the rest to VQE state preparation. We present a new dressing scheme that combines the preservation of the Hamiltonian hermiticity and an exact quadratic truncation of the Baker-Campbell-Hausdorff expansion. The new transformation is constructed as the exponent of an involutory linear combination (ILC) of anti-commuting Pauli products. It incorporates important strong correlation effects in the dressed Hamiltonian and can be viewed as a classical preprocessing step to alleviate the resource requirements of the subsequent VQE application. The assessment of the new computational scheme for the electronic structure of the LiH, H2O, and N2 molecules shows a significant increase in efficiency compared to the conventional qubit coupled cluster dressings.
Eyes wide open: The role of situational information security awareness for security‐related behaviour

Abstract Most contemporary studies on information security focus on largely static phenomena in examining security‐related behaviours. We take a more dynamic, situational and interactionist approach that proposes that security‐related behaviours result from an interaction between the person and the perception of a threatening situation. We derive and define situational information security awareness based on situation awareness literature, and examine how individual‐level (innate traits, experience) and system‐level factors (design variations, warning signal) influence awareness, and how it influences subsequent threat and coping appraisals, and ultimately security‐related behaviours in a multi‐method phishing experiment including eye tracking and survey components with 107 employees. The results underscore the importance of situational information security awareness and show that past experience with phishing and a security warning increase awareness, while phishing emails' contextual relevance and misplaced salience decrease awareness. Situational information security awareness, in turn, increases perceived threat and perceived coping efficacy and, ultimately, actual behavioural responses to phishing attacks.
Fully Decentralized Multi-Party Consent Management for Secure Sharing of Patient Health Records

Patients are becoming aware of the importance of taking secure control and managing access over their medical data, thereby leading to the rise in the adoption of personal health record (PHR) systems. However, today's PHR systems fall short in providing secure and trustable data sharing and access facilities to patients when they are in emergency situations or temporarily incapacitated. Also, the existing PHR systems are centralized and vulnerable to the single point of failure problem. Integrating PHR systems with blockchain technology can help to overcome such limitations. In this paper, we propose a blockchain-based PHR architecture that employs smart contracts to implement multi-party authorization (MPA) and threshold cryptographic schemes to automate secure and trustable medical data sharing and access in PHR systems. Moreover, we mitigate the limited storage and computation capabilities of blockchain by using InterPlanetary File System (IPFS) storage and reputation-governed trusted oracles into the proposed architecture. MPA and threshold cryptographic schemes allow the patient to split and share a secret key with a set of trusted parties, such as the healthcare regulatory agency, guardians, and hospitals, in such a way that they can collectively decide on sharing medical data on behalf of patients. We present algorithms along with their full smart contract function implementation details. We evaluate the robustness and performance of our solution by performing correctness verification and cost analysis. Furthermore, we evaluate the proposed approach in terms of security, generalization, and limitation aspects to find out its feasibility and practicality. We make our smart contract code publicly available on GitHub.
A novel pipeline framework for multi oriented scene text image detection and recognition

Automatic text detection and recognition (end-to-end text recognition) in real-life images are the main elements of many applications including blind and low vision assistance systems and self-driving cars. However, it is challenging to detect curved and vertical texts due to their color bleeding, font size variation, and complicated background. In this paper, a convolutional neural network-based pipeline is introduced to obtain high-level visual features and improve text detection and recognition efficiency. A pre-trained ResNet-50 network on ImageNet and SynthText for extracting low-level visual features was used in this study. Moreover, new improved ReLU layer (new.i.ReLU) blocks are used with a varied receptive field with a strong ability to detect text components even on curved surfaces in the proposed structure. A new improved inception layer (new.i.inception layers) can obtain broadly varying-sized text more effectively than a linear chain of convolution layer. Also, we have proposed a pipeline framework for character recognition that is robust to irregular (curve and vertical) text. First, we introduced a novel algorithm for encoding pixel’s value to a new one called local word directional pattern (LWDP) that highlights the texture of the characters. Then, the output of LWDP was presented as an input image in the text recognition process. The experiments on standard benchmarks, including ICDAR 2013, ICDAR 2015, and ICDAR 2019 datasets, illustrated the superiority of the proposed architecture over prior works.
Cybersecurity knowledge and skills taught in capture the flag challenges

Capture the Flag challenges are a popular form of cybersecurity education, where students solve hands-on tasks in an informal, game-like setting. The tasks feature diverse assignments, such as exploiting websites, cracking passwords, and breaching unsecured networks. However, it is unclear how the skills practiced by these challenges match formal cybersecurity curricula defined by security experts. We explain the significance of Capture the Flag challenges in cybersecurity training and analyze their 15,963 textual solutions collected since 2012. Based on keywords in the solutions, we map them to well-established ACM/IEEE curricular guidelines to understand which skills the challenges teach. We study the distribution of cybersecurity topics, their variance in different challenge formats, and their development over the past years. The analysis showed the prominence of technical knowledge about cryptography and network security, but human aspects, such as social engineering and cybersecurity awareness, are neglected. We discuss the implications of these results and relate them to contemporary literature. Our results indicate that future Capture the Flag challenges should include non-technical aspects to address the current advanced cyber threats and attract a broader audience to cybersecurity.
An adaptive deep learning method for item recommendation system

For many years user textual reviews have been exploited to model user/item representations for enhancing the performance of the Recommender System (RS). However, the traditional methods of the RSs basically rely on the static user/item feature vectors and ignore the fine-grained user–item interactions which could affect the accuracy of the RSs. Thus, this paper proposes a RS model that exploits neural attention techniques to learn adaptive user/item representations and fine-grained user–item interaction for enhancing the accuracy of the item recommendation. An attentive pooling layer is first designed based on the Convolutional Neural Network (CNN) to learn the adaptive latent features of the user/item from reviews. A mutual attention network technique is then introduced for modelling the fine-grained user–item​ interaction to enable jointly capturing the most informative features at the higher granularity. Finally, a prediction layer is then applied for the final prediction based on the adaptive user/item representation and the user/item importance. We extensively conduct a series of experiments using Amazon and Yelp reviews, and the results demonstrate that our proposed model performs better than the existing methods in terms of both rating prediction and ranking performances. Statistical paired test show that all the performance improvements are significant at p<0.05.
Documenting contributions to scholarly articles using CRediT and tenzing

Scholars traditionally receive career credit for a paper based on where in the author list they appear, but position in an author list often carries little information about what the contribution of each researcher was. “Contributorship” refers to a movement to formally document the nature of each researcher’s contribution to a project. We discuss the emerging CRediT standard for documenting contributions and describe a web-based app and R package called tenzing that is designed to facilitate its use. tenzing can make it easier for researchers on a project to plan and record their planned contributions and to document those contributions in a journal article.
SentiX: A Sentiment-Aware Pre-Trained Model for Cross-Domain Sentiment Analysis

Pre-trained language models have been widely applied to cross-domain NLP tasks like sentiment analysis, achieving state-of-the-art performance.However, due to the variety of users' emotional expressions across domains, fine-tuning the pre-trained models on the source domain tends to overfit, leading to inferior results on the target domain.In this paper, we pre-train a sentimentaware language model (SENTIX) via domain-invariant sentiment knowledge from large-scale review datasets, and utilize it for cross-domain sentiment analysis task without fine-tuning.We propose several pre-training tasks based on existing lexicons and annotations at both token and sentence levels, such as emoticons, sentiment words, and ratings, without human interference.A series of experiments are conducted and the results indicate the great advantages of our model.We obtain new state-of-the-art results in all the cross-domain sentiment analysis tasks, and our proposed SENTIX can be trained with only 1% samples (18 samples) and it achieves better performance than BERT with 90% samples.Code is available at https:
Deep Domain Adaptive Object Detection: a Survey

Deep learning (DL) based object detection has achieved great progress. These methods typically assume that large amount of labeled training data is available, and training and test data are drawn from an identical distribution. However, the two assumptions are not always hold in practice. Deep domain adaptive object detection (DDAOD) has emerged as a new learning paradigm to address the above mentioned challenges. This paper aims to review the state-of-the-art progress on deep domain adaptive object detection approaches. Firstly, we introduce briefly the basic concepts of deep domain adaptation. Secondly, the deep domain adaptive detectors are classified into five categories and detailed descriptions of representative methods in each category are provided. Finally, insights for future research trend are presented.
A modified equilibrium optimizer using opposition-based learning and novel update rules

Equilibrium Optimizer (EO) is a newly developed physics-based metaheuristic algorithm that is based on control volume mass balance models, and has shown competitive performance with other state-of-the-art algorithms. However, the original EO has the disadvantages of a low exploitation ability, ease of falling into local optima, and an immature balance between exploration and exploitation. To address these shortcomings, this paper proposes a modified EO (m-EO) using opposition-based learning (OBL) and novel update rules that incorporates four main modifications: the definition of the concentrations of some particles based on OBL, a new nonlinear time control strategy, novel population update rules and a chaos-based strategy. Based on these modifications, the optimization precision and convergence speed of the original EO are greatly improved. The validity of m-EO is tested on 35 classical benchmark functions, 25 of which have variants belonging to multiple difficulty categories (Dim = 30, 100, 300, 500 and 1000). In addition, m-EO is used to solve three real-world engineering design problems. The experimental results and two different statistical tests demonstrate that the proposed m-EO shows higher performance than original EO and other state-of-the-art algorithms.