Reviewerly/exHarmony huggingface dataset:
Document-level relation extraction with two-stage dynamic graph attention networks

Document-level Relation Extraction (RE) aims to infer complex semantic relations between entities in a document. Previous approaches leverage a multi-classification model to predict relation types between each entity pair. However, in contrast to sentence-level RE, document-level RE contains various entities expressed by mentions appearing across multiple sentences in a document. Therefore, the amount of negative instances (‘no relationship’) significantly outnumbers that of other positive instances in document-level RE. In addition, most existing methods construct static graphs with heuristic rules to capture the interactions among entities. However, these heuristic rules ignore the specificities of the documents. In this study, we propose a novel two-stage framework to extract document-level relations based on dynamic graph attention networks, namely TDGAT. In the first stage, we capture the relational links of the entity pairs using a binary classification model. In the second stage, we extract fine-grained relations among entities, including the type of ‘NA (no relationship)’. To reduce error propagation, we regard the entity pair links predicted in the first stage as the prior information and leverage them to reconstruct the document-level graphs of the second stage. In this manner, we can provide extra head and tail entity connection information for predicting relations in a document. Furthermore, we propose a dynamic graph strategy to explore the multi-hop interactions between related information. The experimental results show that our framework outperforms most existing models on the public document-level dataset DocRED. The extensive analysis demonstrates the effectiveness of our TDGAT in extracting inter-sentence relations.
How Corruption Mitigates the Effect of FDI on Economic Growth?

Unlike previous studies in the foreign direct investment-economic growth literature, this study uses the panel vector autoregressive (PVAR) model to examine the role of corruption in inhibiting the effect of foreign direct investment on the African economic growth. Furthermore, we use the impulse response function tool to better understand the reaction of economic growth, after shock on foreign direct investment, and the interaction between foreign direct investment and corruption. Using data over the period 1996–2016, this research results show that the foreign direct investment promotes the African economic growth. While corruption mitigates this effect, therefore, the implications of this paper are that public policies should aim to minimize the level of corruption in order to ameliorate the attractiveness of FDI and ensure its efficient utilization in order to give strength to the level of economic growth.
An Interactive and Reductive Graph Processing Library for Edge Computing in Smart Society

Due to the limitations of cloud computing on latency, bandwidth and data confidentiality, edge computing has emerged as a novel location-aware paradigm to provide them with more processing capacity to improve the computing performance and quality of service (QoS) in several typical domains of human activity in smart society, such as social networks, medical diagnosis, telecommunications, recommendation systems, internal threat detection, transports, Internet of Things (IoT), etc. These application domains often handle a vast collection of entities with various relationships, which can be naturally represented by the graph data structure. Graph processing is a powerful tool to model and optimize complex problems in which the graph-based data is involved. In view of the relatively insufficient resource provisioning of the portable terminals, in this paper, for the first time to our knowledge, we propose an interactive and reductive graph processing library (GPL) for edge computing in smart society at low overhead. Experimental evaluation is conducted to indicate that the proposed GPL is more user-friendly and highly competitive compared with other established systems, such as igraph, NetworKit and NetworkX, based on different graph datasets over a variety of popular algorithms.
Deep Learning Methods for Arabic Autoencoder Speech Recognition System for Electro-Larynx Device

Recent advances in speech recognition have achieved remarkable performance comparable with human transcribers’ abilities. But this significant performance is not the same for all the spoken languages. The Arabic language is one of them. Arabic speech recognition is bounded to the lack of suitable datasets. Artificial intelligence algorithms have shown promising capabilities for Arabic speech recognition. Arabic is the official language of 22 countries, and it has been estimated that 400 million people speak the Arabic language worldwide. Speech disabilities have been one of the expanding problems in the last decades, even in kids. Some devices can be used to generate speech for those people. One of these devices is the Servox Digital Electro-Larynx (EL). In this research, we developed an autoencoder with a combination of long short-term memory (LSTM) and gated recurrent units (GRU) models to recognize recorded signals from Servox Digital EL Electro-Larynx. The proposed framework consisted of three steps: denoising, feature extraction, and Arabic speech recognition. The experimental results show 95.31% accuracy for Arabic speech recognition with the proposed model. In this research, we evaluated different combinations of LSTM and GRU for constructing the best autoencoder. A rigorous evaluation process indicates better performance with the use of GRU in both encoder and decoder structures. The proposed model achieved a 4.69% word error rate (WER). Experimental results confirm that the proposed model can be used for developing a real-time app to recognize common Arabic spoken words.
The Outlook of Non Fungible Tokens (NFTs): an alternative for academic manuscript ownership and scholarly publications

scientific research is expanding at an astounding rate, introducing new and compounding existing challenges and exacerbating those that already exist with regard to peer review quality, scholarly publishing and the emergence of predatory journals, and challenges with academic integrity and frauds in publication. To address this problem, this article suggests using NFT (Non Fungible Token) as a new, less costly, and more equitable means of publishing when it comes to intangible property ownership and identification. Blockchain enables autonomous, decentralized data, eventually resulting in a shared infrastructure where transactions are recorded and preserved. Our method covers the whole spectrum of NFT capabilities for academic manuscripts and scholarly publications through a decentralised solution using open-access tools
Digital Forensic Accounting and Cyber Fraud in Nigeria

Cyber fraud is a growing menace in Nigeria. Improper investigations and failure to produce substantial evidence on the activities of cyber fraudsters have limited the number of successful prosecutions, compared to the number of cyber fraudsters on the web in the country. This study examines the association between digital forensic accounting and cyber fraud. Primary source was used in gathering data, through a survey questionnaire. The population of the study comprised of accounting professionals (excluding partners) in the “Big Four” accounting firms in Nigeria. The study employed correlation as the technique of data analysis. The results revealed that digital forensic accounting has a negative and significant association with phishing scams, advance fee fraud and credit card fraud. Hence, the use of digital forensic accounting, therefore, minimises the incidence of cyber frauds in Nigeria. The study recommended that forensic accountants should embrace digital forensic investigation processes in cases involving cyber frauds. In addition, the Nigerian Government, through the anti-graft institutions should adopt digital forensics in examining cases on cyber fraud and related offences; for smooth flow of investigations and robust evidence to facilitate successful prosecutions in the courtroom.
A complex network-based firefly algorithm for numerical optimization and time series forecasting

The firefly algorithm (FA) has gained widespread attention and has been widely applied because of its simple structure, few control parameters and easy implementation. As the traditional FA lacks a mutation mechanism, it tends to fall into local optima, leading to premature convergence, thus affecting the optimization accuracy. To address these limitations, from the perspective of population diversity, a complex network-based FA (CnFA) with scale-free properties is proposed in this paper. The scale-free properties of complex networks effectively ensure the diversity of populations to guide the populations in their search, thus avoiding random interactions of information among populations that could lead to superindividuals controlling the entire population. The property of the power-law distribution of nodes in complex networks is exploited to effectively avoid the premature convergence of the FA and falling into local optima. To verify the search performance of CnFA, we compared the FA and its variants, as well as multiple competitive approaches, on 30 different-dimension benchmark function optimization tasks and two time series prediction tasks. The experimental results and statistical analysis show that CnFA achieves satisfactory performance due to the better balance between exploitation and exploration in the search process. Additionally, we extended the proposed method to two other population-based algorithms, and the experimental results verify that the complex network-based mechanism can enhance the performance of not only the FA but also other population-based evolutionary algorithms.
Language and vision based person re-identification for surveillance systems using deep learning with LIP layers

Real-time surveillance systems have become a necessity of today's life owing to their relevance in the contemporary era for security reasons to ensure a secure and safe environment. Presently, Person re-identification (Re-ID)-based surveillance systems are becoming increasingly more prevalent and sophisticated since they do not require human intervention and are more reliable to deploy in public spaces leveraging multi-camera networks. However, one of the major problems in Person ReID is the visual appearance i-e the appearance of a person in an image is greatly affected by different camera views. As a result, the discriminative set of features must be learned in a deep learning model in order to re-identify persons from opposing camera viewpoints. To address this challenge, we propose an image/text-retrieval-based Person ReId method in which both visual and text-based features are exploited to carry out person re-identification. More precisely, the textual descriptions of the images are taken into account as text features with Glove Word Embedding followed by 1D-MAPCNN and fused with image-level features extracted using the GoogLeNet model. In addition, the feature discriminability is enhanced using local importance-based pooling (LIP) layers in which adaptive significance weights are learned during downsampling. Moreover, from two different modalities, feature refinement is done during training with the help of attention mechanisms using the Convolutional Block Attention module (CBAM) and the proposed shared attention neural network. It is observed that LIP layers along with both vision and textual features are playing a key role in acquiring discriminative features even if the visual appearance of the same person is greatly affected due to camera pose conditions. The proposed method is validated on the CUHK-PADES dataset and has 15.34% and 24.39% rank-1 improvement in text and image-based retrievals.
Secondary studies on human aspects in software engineering: A tertiary study

This study compiles the evidence reported on the human aspects of software engineering in view of providing a comprehensive catalogue of human aspects that have been examined. To summarise the existing systematic literature on human aspects in software engineering. This study employs published tertiary research guidelines to investigate secondary studies published between 1940 and 2021. We identified 67 secondary studies concentrating on 16 different human aspects research categories, including Agile, Economic Factors, Environmental Factors to Productivity, Success, and Teams. Several trends reveal the topics that have received the least (e.g., “software engineer controllers”) and most (e.g., “individual human aspects”) attention in research. Outcomes show that the number of secondary studies on human aspects in software engineering continues to rise when compared to other software engineering topics, despite experiencing a significant drop in 2020. Many secondary studies implemented established guidelines, especially those published in scholarly journals. While there is variance in the quality of published secondary studies, the average quality score across the investigated studies was 3.09 out of 4.0, with journal-published studies and one thesis having higher quality than conference and workshops papers. Specific institutions are also more central to the publication of secondary studies. Furthermore, there has been noteworthy advancement in the consideration of human aspects across the domain. Finally, we discovered several relationships among human aspects investigated. For example, the “Industry” subject of investigation is strongly correlated with the “Theoretical” study type. The overview provided by this study allows researchers and practitioners to gain familiarity with the current state of research on human aspects, helping researchers to identify gaps for further study and allowing practitioners to discover high-quality, evidence-based approaches to harness the power of human aspects in software engineering.
Modeling and Simulation of an Octorotor UAV with Manipulator Arm

In this paper, the conceptual design, modeling, and simulation are proposed for an octorotor UAV with a manipulator arm. The conceptual design of the octorotor UAV with a manipulator arm is developed, and for the study and analysis, the design is implemented and validated in Matlab (Simulink-SimMechanics) software. The kinematics and dynamics models of the octorotor UAV with a manipulator arm are obtained using the classical Denavit–Hartenberg convention and the recursive Newton–Euler method, respectively. In this sense, a cascade PID controller for the attitude and navigation of the UAV and a simple PID controller for the manipulator arm are proposed and simulated in a closed-loop system in order to highlight the performance of the proposed design. Finally, simulations show the feasibility and behavior of the mathematical model and the flight controller in a closed-loop system.
Algorithm improvement for the surface morphology diagnostics based on the Gram-Schmidt orthonormalization and the least square ellipse fitting under the EAST-like vibrational environments

To realize in situ surface morphology diagnosis of Plasma-Facing Materials (PFMs) in the Experimental Advanced Superconducting Tokamak (EAST), a diagnostic system which is called DUT-SIEP (Speckle Interferometry Experiment Platform in Dalian University of Technology) is in development. Mimicking the actual vibrational conditions in EAST, Dual-wavelength Phase-Shifting Interferometry (DPSI) should be sensitive to vibrational environment and would reduce accuracy in the morphology measurements. Therefore, an approach combining the Gram–Schmidt orthonormalization and the Least-Square Ellipse Fitting (GS-LSEF) was proposed to replace the conventional phase extraction process to improve the measurement accuracy in the EAST-like vibration conditions. The new approach only requires two interferograms at each wavelength while the conventional method needs three at least for each wavelength. This significantly reduces the phase errors in the acquisition processes. The Gram–Schmidt orthonormalization is performed to correct the phase shift errors and determine the elliptic coefficients. In the Least-Square Ellipse Fitting, the elliptic coefficients are used to compensate the phase errors from the disturbed background intensity. The approach can significantly improve the measurement efficiency and avoid the infinite loops caused by the numeric divergent. The method was verified by laser ablation on tungsten (W) material for simulating erosion-processes in EAST. Results show that the proposed approach has significantly improved in accuracy by approximately twofold comparing with those by the convenient Four-step phase-shifting approaches due to reducing the noise superposition problems in the EAST-like vibrational conditions.
NFT Image Plagiarism Check Using EfficientNet-Based Deep Neural Network with Triplet Semi-Hard Loss

Blockchain technology is used to support digital assets such as cryptocurrencies and tokens. Commonly, smart contracts are used to generate tokens on top of the blockchain network. There are two fundamental types of tokens: fungible and non-fungible (NFTs). This paper focuses on NFTs and offers a technique to spot plagiarism in NFT images. NFTs are information that is appended to files to produce distinctive signatures. It can be found in image files, real artifacts, literature published online, and various other digital media. Plagiarism and fraudulent NFT images are becoming a big concern for artists and customers. This paper proposes an efficient deep learning-based approach for NFT image plagiarism detection using the EfficientNet-B0 architecture and the Triplet Semi-Hard Loss function. We trained our model using a dataset of NFT images and evaluated its performance using several metrics, including loss and accuracy. The results showed that the EfficientNet-B0-based deep neural network with triplet semi-hard loss outperformed other models such as Resnet50, DenseNet, and MobileNetV2 in detecting plagiarized NFTs. The experimental results demonstrate sufficient to be implemented in various NFT marketplaces.
Blockchain-Based Decentralized Architecture for Software Version Control

Version control is an important component of configuration management, and most enterprise-level software uses different tools and technologies to manage the software version control such as CVS, Subversion, or Perforce. Following the success of bitcoin, the first practical application of blockchain, it is being implemented in other fields such as healthcare, supply chains, financial management, real estate, electoral systems, and so on. Blockchain’s core features include decentralization, immutability, and interminability. Most version control repositories are centralized and can be modified by external sources, implying that they are in danger of being corrupted or controlled. In this study, we present the BDA-SCV architecture for implementing a version control system in blockchain technology. Our proposed approach would replace the necessity for a centralized system, with a decentralized approach implemented in the blockchain using distributed file storage, for which we will use the InterPlanetary File System (IPFS), which is a distributed file system. The proof of authority (PoA) consensus algorithm will be used to approve the developer communicating modifications to the private blockchain network; the authority will only provide permission and will not be able to add, edit, or delete code files. For each change, a ledger block will be created with a reference to the file stored in the distributed repository. A block cannot be manipulated once it has been created. Smart contracts will be used to register developers, create blocks, and manage the repository. The suggested model is implemented using the Hyperledger Fabric network, and the developer and authorizer ends are built into the dotnet web application.
Inspection path planning of free-form surfaces based on improved cuckoo search algorithm

To address the problems of long run times, long path length and low efficiencies of traditional intelligent algorithms to optimise free-form surface inspection path algorithms, this paper proposes a method based on an improved cuckoo search algorithm. Since the basic cuckoo search algorithm suffers from problems such as low search efficiency and the tendency to fall into local optimum solutions, the basic cuckoo search algorithm is improved by using a parameter adaptive adjustment strategy and dynamic neighbourhood search strategy, so that the improved cuckoo search algorithm can obtain the optimised inspection path stably and quickly. The local composition of the free-form surface inspection path and the corresponding mathematical model are first analysed, and then traditional intelligent algorithms and the improved cuckoo search algorithm are applied to optimise the mathematical model. The results of inspection experiments conducted with an engine impeller showed that the improved cuckoo search algorithm reduced the length of the optimised inspection path by at least 8.6%, reduced the algorithm run time by at least 35%, and improved the inspection efficiency by at least 1.2% compared to those of the genetic algorithm, simulated annealing algorithm, and ant colony Optimisation algorithm. The improved cuckoo search algorithm allows for effective free-form surface inspection path Optimisation and an improved inspection efficiency.
Using Natural Language Processing to Analyze Political Party Manifestos from New Zealand

This study explores how natural language processing (NLP) can supplement content analyses of political documents, particularly the manifestos of political parties. NLP is particularly useful for tasks such as: estimating the similarity between documents, identifying the topics discussed in documents (topic modeling), and sentiment analysis. This study applies each of these techniques to the study of political party manifestos. Document similarity may be used to gain some insight into the way parties change over time and which political parties are successful at bringing attention to their policy agenda. Categorizing text into topics may help objectively categorize and visualize the ideas political parties are discussing. Finally, sentiment analysis has the potential to show each political party’s attitude towards a policy area/topic. This study specifically applies these techniques to the manifestos produced by the political parties of New Zealand, from 1987 to 2017 (a period of significant party system change in New Zealand). It finds that NLP techniques provide valuable insights, although there is a need for significant fine-tuning.
XAI enhancing cyber defence against adversarial attacks in industrial applications

In recent years there is a surge of interest in the interpretability and explainability of AI systems, which is largely motivated by the need for ensuring the transparency and accountability of Artificial Intelligence (AI) operations, as well as by the need to minimize the cost and consequences of poor decisions. Another challenge that needs to be mentioned is the Cyber security attacks against AI infrastructures in manufacturing environments. This study examines eXplainable AI (XAI)-enhanced approaches against adversarial attacks for optimizing Cyber defense methods in manufacturing image classification tasks. The examined XAI methods were applied to an image classification task providing some insightful results regarding the utility of Local Interpretable Model-agnostic Explanations (LIME), Saliency maps, and the Gradient-weighted Class Activation Mapping (Grad-Cam) as methods to fortify a dataset against gradient evasion attacks. To this end, we “attacked” the XAI-enhanced Images and used them as input to the classifier to measure their robustness of it. Given the analyzed dataset, our research indicates that LIME-masked images are more robust to adversarial attacks. We additionally propose an Encoder-Decoder schema that timely predicts (decodes) the masked images, setting the proposed approach sufficient for a real-life problem.