Reviewerly/exHarmony huggingface dataset:
Evaluation of an Arabic Chatbot Based on Extractive Question-Answering Transfer Learning and Language Transformers

Chatbots are programs with the ability to understand and respond to natural language in a way that is both informative and engaging. This study explored the current trends of using transformers and transfer learning techniques on Arabic chatbots. The proposed methods used various transformers and semantic embedding models from AraBERT, CAMeLBERT, AraElectra-SQuAD, and AraElectra (Generator/Discriminator). Two datasets were used for the evaluation: one with 398 questions, and the other with 1395 questions and 365,568 documents sourced from Arabic Wikipedia. Extensive experimental works were conducted, evaluating both manually crafted questions and the entire set of questions by using confidence and similarity metrics. Our experimental results demonstrate that combining the power of transformer architecture with extractive chatbots can provide more accurate and contextually relevant answers to questions in Arabic. Specifically, our experimental results showed that the AraElectra-SQuAD model consistently outperformed other models. It achieved an average confidence score of 0.6422 and an average similarity score of 0.9773 on the first dataset, and an average confidence score of 0.6658 and similarity score of 0.9660 on the second dataset. The study concludes that the AraElectra-SQuAD showed remarkable performance, high confidence, and robustness, which highlights its potential for practical applications in natural language processing tasks for Arabic chatbots. The study suggests that the language transformers can be further enhanced and used for various tasks, such as specialized chatbots, virtual assistants, and information retrieval systems for Arabic-speaking users.
Digital Competence Development in Public Administration Higher Education

Today, citizens’ digital readiness and competence are competitive factors. While users with a high level of digital competence are usually associated with intensive and conscious use by consumers, those with a lower level of competence are characterized by passive consumer use and lagging in technology. It is essential to continuously develop and monitor competence levels because digital skills, the way digital tools are used, and the effects of use are interlinked. The importance of digital competence is also evident in public administration because its presence affects the quality of public services, which has an impact on the entire population. In this social aspect, digital competence is extremely important for public administration employees. In our study, we aim to investigate the input competence level of undergraduate (BA) students in public administration, and the output competence level reached after one semester in targeted training. We analyze the values in several dimensions and draw conclusions. When measuring digital competence, we relied on the EU reference material, the Citizen Digital Competence Framework, based on digital competence areas and proficiency levels such as information and data literacy, communication and collaboration, digital content creation, safety, and problem solving. The DigCompSat self-assessment questionnaire was used for the survey. The result of the study was an order of magnitude difference between the self-assessment and the real level of competence of the students. It can be useful for respondents to realize where they have gaps in their knowledge and competence. This is what is referred to as a ’change in attitude‘ in our study. There are factors related to gender, age, and prior training that influence the potential shifts between the segments. It was also found that the above factors applied differently in each range of competence. Concerning implementation of this research, it can be assumed that the larger amplitude movements may not necessarily be knowledge or skills, but rather ’changes in attitude’. Its practical importance might lie in the possibility to orient students.
Deep learning forecasting tool facilitating the participation of photovoltaic systems into day-ahead and intra-day electricity markets

The participation of photovoltaic (PV) power into the energy market requires the development of accurate forecasting models to alleviate the imbalance penalties and ensure the viability of the investments. Several forecasting models have been proposed, focusing on different forecasting horizons. However, their implementation does not consider the energy market rules. Here we present a Day-Ahead Market (DAM)/Intra-Day Market (IDM) forecasting tool which comprises of four individual Deep Learning (DL) forecasters to facilitate the participation of PV power into the DAM and the three auctions of IDM. Different combinations of historical and predicted data of cloud cover and temperature are examined on Convolutional Neural Network and Transformer models for the forecasters’ development. The proposed tool is tested on five PV systems, while the aggregated production is also examined considering their spatial characteristics. The results indicate that the proposed forecasting tool could decrease the Mean Absolute Error up to 31.96% and 32.45% for DAM and IDM respectively.
Random forest classification for volcanogenic massive sulfide mineralization in the Rouyn-Noranda Area, Quebec

Random forest (RF) classification was applied to 37 predictor maps (vectors to mineralization) producing a Mineral Prospectivity Map (MPM) for volcanogenic massive sulfide (VMS) mineralization in the Noranda District, Abitibi subprovince, which is host to ∼20 VMS deposits and numerous subeconomic occurrences. The predictor maps were created using geological, geochemical, and geophysical data, and the known VMS deposits were used to train the RF classifier. The RF model was applied on two regions of interest (ROI) to investigate the effect of different sized ROIs on the results. Five sets of balanced and unbalanced training data were used in the experiments to examine the sensitivity of RF to different sets of training data. The probability/prospectivity maps showed very high success rate of classification with regard to training data particularly for the training sets with higher ratio of non-deposits/deposits. Accuracy assessment of classification maps using cross-validation also showed very high accuracy when assessed by the training data in all experiments again giving a higher accuracy for the training sets with higher ratios of non-deposits/deposits, however the later showed very low accuracy when applying k-fold cross-validation or when assessed by 15 VMS showings used as the test data indicating an overfitting problem with the unbalanced data. In most of the experiments, principal component 4 (PC4) of geochemical analysis, proximity to synvolcanic tonalite-trondhjemite-granodiorite (TTG) intrusions, lithology, and sericite alteration maps showed higher predictive power. The importance of individual variables changed when the ROI was changed to a smaller area suggesting that RF is sensitive to the study area.
The complete new solutions to the blind millionaires’ problem in d-dimensional quantum system

The blind millionaires' problem is an extension of the millionaires' problem which compares the value of the secret summation kept by any number of participants between two sets. Recently, some attempts are proposed to solve the blind millionaires' problem of comparing the value of the secrets summation for identical number of the participants. In this paper, the complete solution to blind millionaires' problem is presented with d-level quantum technologies for the first time. During it, the comparison can be realized for the secret summation between any p participants in set A and q participants in set B. Derived from that, the solution can be expressed with the following two cases: the overall comparison between two sets with different number of participants; the local comparison between one participant in set A/B and the whole participants in set B/A. According to our analysis, it can be seen that the proposed solution is secure against the known attacks and the secrets cannot be leaked to the external and internal attackers.
A New GNN-Based Object Detection Method for Multiple Small Objects in Aerial Images

Detecting small objects in aerial images is challenging due to limited information of the object and complex backgrounds surrounding them. Most of the existing object detection methods focus on detecting individual objects and have different feature extraction methods based on object appearance. In this paper, we propose a new Graph Neural Networks (GNN) based method to refine detection results generated by object detectors. In this method, we construct a detection graph by using the predicted detection bounding boxes as nodes, while the features of a bounding box become its node features. Edges are added using distance and topological information. Then, based on the detection confidences of the bounding boxes, some nodes are labeled as bird or non-bird, where nodes with high detection confidences are labelled bird and nodes with very low detection confidences are labeled as non-bird. GNN algorithms are trained using the labeled portion of the graph to infer the classes of the unlabeled nodes. Our experimental results on detecting waterfowl in aerial images show that the new method significantly improved detection accuracy and robustness by significantly reducing false positives.
Tuning of fuzzy controller with arbitrary triangular input fuzzy sets based on proximal policy optimization for time-delays system

The analytical structure of fuzzy controller is important for better understanding and effective design of the fuzzy control system, especially for explaining why fuzzy control system work well. In this paper, we develop a three-dimensional fuzzy controller with arbitrary triangular input fuzzy sets. Then, the analytical structure of this fuzzy controller is derived rigorously, and some properties of this controller are given. To determine the parameters of the fuzzy controller, a novel proximal policy optimization algorithm with recall pool (RP-PPO) is proposed. A recall pool is designed in proximal policy optimization algorithm to store the best ten sets of parameters to enhance the optimization results. Finally, simulation studies are carried out on two time-delay systems and a nonlinear chemical process with time-varying time delay to validate the applicability and effectiveness of the proposed fuzzy controller.
Continuous time causal structure induction with prevention and generation

Most research into causal learning has focused on atemporal contingency data settings while fewer studies have examined learning and reasoning about systems exhibiting events that unfold in continuous time. Of these, none have yet explored learning about preventative causal influences. How do people use temporal information to infer which components of a causal system are generating or preventing activity of other components? In what ways do generative and preventative causes interact in shaping the behavior of causal mechanisms and their learnability? We explore human causal structure learning within a space of hypotheses that combine generative and preventative causal relationships. Participants observe the behavior of causal devices as they are perturbed by fixed interventions and subject to either regular or irregular spontaneous activations. We find that participants are capable learners in this setting, successfully identifying the large majority of generative, preventative and non-causal relationships but making certain attribution errors. We lay out a computational-level framework for normative inference in this setting and propose a family of more cognitively plausible algorithmic approximations. We find that participants' judgment patterns can be both qualitatively and quantitatively captured by a model that approximates normative inference via a simulation and summary statistics scheme based on structurally local computation using temporally local evidence.
Agency in the algorithmic age: The mechanisms and structures of blockchain-based organizing

Firms are increasingly adopting blockchains as a form of knowledge engineering to align their internal processes, share information, and improve oversight. We study intraorganizational blockchains from an agency perspective to understand how the distributed, sequenced, and consensus-based nature of blockchains mitigates information asymmetries and affects organizational structures. First, we explain how blockchains differ from conventional relational, contractual, and system-based mechanisms to address three pertinent information challenges—concentration, continuity, and conflict. Subsequently, we address the consequences of introducing such blockchains, arguing that they create both direct and sequenced information channels among principals and agents, which elicit an organizational reconfiguration via vertical disintermediation and lateral reintermediation. Finally, we theorize the implications of blockchain-based organizing for agency theory regarding the chain of command, the unity of direction, and the span of control. Overall, we show how blockchains for intraorganizational governance can mitigate principal-agent problems and impact organizational design in profound ways.
The welch-gong stream cipher - evolutionary path

Abstract This survey presents the rich history of the Welch-Gong (WG) Stream cipher family. It has been a long journey that lead the WG stream ciphers to become practical. The evolutionary path is a combination of mathematical endeavour and engineering striving to transfer pure mathematical functions to practical encryption algorithms for various applications. This path began as the pioneering work on WG transformation sequences with 2-level autocorrelation, leading to important breakthroughs in the early 2000’s, such as the submission of the first WG stream cipher to the eSTREAM competition in 2005 and the subsequent introduction of the WG stream cipher family WG ( m , l ), followed by extensive work on particular instances proposed for various (mostly lightweight) applications. A recent construction using a WG permutation is the authenticated encryption WAGE, submitted to the NIST LWC competition in 2019. The story of the WG stream cipher is by far not finished. The future opens numerous possibilities for WG stream ciphers and WAGE, with applications in both lightweight environments and in high-performance computing. We conclude the survey with new ideas and open problems.
Infrastructure Quality and FDI Inflows: Evidence from the Arrival of High-Speed Internet in Africa

Abstract Does ambient infrastructural quality affect foreign direct investment (FDI) in developing countries? This paper investigates how the arrival of high-speed internet in Africa triggered FDI into the region. It also explores the role of complementary infrastructure, such as access to electricity and road connectivity, in amplifying the impact of internet connectivity on investment. To causally estimate impacts, the paper exploits plausibly exogenous variations in access to high-speed internet induced by the staggered arrival of submarine fiber-optic internet cables and spatial variations in terrestrial fiber cable networks across locations on the continent. Findings from the paper indicate that access to high-speed internet induces FDI, particularly in the service sector, with the finance, technology, retail, and health services subsectors as the main beneficiaries. Access to (hard) infrastructure, such as electricity and roads, amplifies the impact of internet connectivity on FDI, thus highlighting the role of complementarities in the impact of infrastructure. Further, the results suggest that improvement in quality of governance and increased performance of incumbent firms are plausible mechanisms.
Federated Learning Meets Blockchain to Secure the Metaverse

The development of the Metaverse is completely changing how business is done in the physical world. The Metaverse considerably improves intelligent manufacturing by mapping out operations and spreading them into virtual space. The Metaverse can access data from numerous production and operation lines thanks to the Internet of Things (IoT), enabling efficient data analysis and decision-making. However, the problem of sharing sensitive and private data remains a challenge when integrating the Metaverse with IoT. Federated learning (FL) has emerged as a distributed machine learning (ML) setting that can overcome the security problems related to data sharding With FL, several devices can work together to create an ML model under the direction of a central server while maintaining the privacy and security of their local training data. FL in the Metaverse continues to face significant challenges due to a lack of transparency, learning forgetting caused by streaming industrial data, and problems with non-independent and identically dispersed (non-iid) data. In this paper, we develop a FL framework for transparent and secure model learning in the Metaverse using blockchain technology. The blockchain ledger stores and verifies the model updates which ensures that all updates are tamper-proof and transparent to all parties involved. Furthermore, we propose a scheduling approach to distribute the bandwidth between reliable devices, hence minimizing communication across FL devices and giving devices with reliable behavior priority. The numerical result demonstrates that our framework performed better on the chosen indicators.
MedLens: Improve Mortality Prediction Via Medical Signs Selecting and Regression

Monitoring the health status of patients and predicting mortality in advance is vital for providing patients with timely care and treatment. Massive medical signs in Electronic Health Records (EHR) are fitted into advanced machine learning models to make predictions. However, the data-quality problem of original clinical signs is less discussed in the literature. Based on an in-depth measurement of the missing rate and correlation score across various medical signs and a large amount of patient hospital admission records, we discovered the comprehensive missing rate is extremely high, and a large number of useless signs could hurt the performance of prediction models. Then we concluded that only improving data-quality could improve the baseline accuracy of different prediction algorithms. We designed MEDLENS, with an automatic vital medical signs selection approach via statistics and a flexible interpolation approach for high missing rate time series. After augmenting the data-quality of original medical signs, MEDLENS applies ensemble classifiers to boost the accuracy and reduce the computation overhead at the same time. It achieves a very high accuracy performance of 0.96% AUC-ROC and 0.81% AUC-PR, which exceeds the previous benchmark.
Enhanced Hierarchical and Sequential Covariance Intersection Fusion

Covariance intersection (CI) fusion is one of the most popular methods for combining estimates when the correlations among local estimation errors are unknown. Considering practical communication constraints, CI fusion tends to be performed in hierarchical and sequential forms, i.e., hierarchical CI (HCI) fusion and sequential CI (SCI) fusion. However, existing HCI and SCI fusion are sensitive to some uncertainties, i.e., the hierarchy structure and the fusion order, which make their fusion performances unreliable. To solve this problem, this article proposes hierarchy-structure-independent HCI fusion and fusion-order-independent SCI fusion by analogy with batch CI fusion, which can avoid possible negative effects caused by the aforementioned uncertainties. Finally, two simulations verify the effectiveness and advantages of the proposed methods.
A Novel Framework for Smart Cyber Defence: A Deep-Dive Into Deep Learning Attacks and Defences

Deep learning techniques have been widely adopted for cyber defence applications such as malware detection and anomaly detection. The ever-changing nature of cyber threats has made cyber defence a constantly evolving field. Smart manufacturing is critical to the broader thrust towards Industry 4.0 and 5.0. Developing advanced technologies in smart manufacturing requires enabling a paradigm shift in manufacturing, while cyber-attacks significantly threaten smart manufacturing. For example, a cyber attack (e.g., backdoor) occurs during the model’s training process. Cyber attack affects the models and impacts the resultant output to be misled. Therefore, this paper proposes a novel and comprehensive framework for smart cyber defence in deep learning security. The framework collectively incorporates a threat model, data, and model security. The proposed framework encompasses multiple layers, including privacy and protection of data and models. In addition to statistical and intelligent model techniques for maintaining data privacy and confidentiality, the proposed framework covers the structural perspective, i.e., policies and procedures for securing data. The study then offers different methods to make the models robust against attacks coupled with a threat model. Along with the model security, the threat model helps defend the smart systems against attacks by identifying potential or actual vulnerabilities and putting countermeasures and control in place. Moreover, based on our analysis, the study provides a taxonomy of the backdoor attacks and defences. In addition, the study provides a qualitative comparison of the existing backdoor attacks and defences. Finally, the study highlights the future directions for backdoor defences and provides a possible way for further research.
A Novel Coarse-to-Fine Deep Learning Registration Framework for Multimodal Remote Sensing Images

Multi-modal remote sensing images with large rotation transformation (RT) are challenging to be registered. It needs to deal with the global geometric deformation caused by great RT and significant local appearance differences caused by different imaging mechanisms. Existing deep learning methods mainly use a single deep descriptor learning (DDL) network to extract invariant features for identifying matching samples and discriminative feature descriptors for separating non-matching samples. However, it is difficult to extract local invariant feature descriptors to RT and modality change through a single DDL network. This paper proposes a novel coarse-to-fine deep learning image registration framework for multi-modal remote sensing images based on two task-specific deep models. Specifically, in the coarse registration stage, this paper designs an effective deep ordinal regression (DOR) network for rotation correction, which can reduce the difficulty of multi-modal image registration and boost image registration. The proposed DOR network transforms the rotation correction task into a rotation ordinal regression problem, which can exploit the potential relationship between the rotation ordinals to improve the accuracy of rotation estimation. In the fine registration stage, we adopt the DDL network to deal with the image modality change based on the rotation-corrected images. Extensive experimental results on multi-modal image datasets demonstrate the significant advantages of the proposed coarse-to-fine deep learning registration framework. The DOR network achieves higher rotation correction accuracy, which can significantly improve the multi-modal image registration performances.