Reviewerly/exHarmony huggingface dataset:
Reducing the dependency of having prior domain knowledge for effective online information retrieval

Abstract Sometimes Internet users struggle to find what they are looking for on the Internet due to information overload. Search engines intend to identify documents related to a given keyphrase on the Internet and provide suggestions. Having some background knowledge about a topic or a domain will help in building effective search keyphrases that will lead to accurate results in information retrieval. This is further pronounced among students that rely on the internet to learn about a new topic. Students might not have the required background knowledge to build effective keyphrases and find what they are looking for. In this research, we are addressing this problem, and aim to help students find relevant information online. This research furthers existing literature by enhancing information retrieval frameworks through keyphrase assignment, aiming to expose students to new terminologies, therefore reducing the dependency of having background knowledge about the domain under study. We evaluated this framework and identified how it can be enhanced to suggest more effective search keyphrases. Our proposed suggestion is to introduce a keyphrase Ranking Mechanism that will improve the keyphrase assignment part of the framework by taking into consideration the part‐of‐speech of the generated keyphrases. To evaluate the proposed approach, various data sets were downloaded and processed. The results obtained showed that our proposed approach produces more effective keyphrases than the existing framework.
A Trust-Aware Task Allocation Method Based on Blockchain for The Internet of Things

In IoT systems, evaluating the trustworthiness of users and devices before relying on their information is a significant problem. Preserving privacy, decentralization, and self-management without the need of a third party is an open problem in trust management in IoT systems. Collecting reputation feedback and shreds of evidence can also be a source of security issues. On the other hand, blockchain is a promising approach for such environments where centralization and trusted third parties can cause security issues. We present a method by extending Contract Net Protocol for task allocation, which can be exploited as a base for trust management in IoT systems. This method allocates tasks to participants through smart contracts, and feedback values are securely collected in the blockchain. After registration of devices, all communications among IoT devices are handled by smart contracts, and reputation values are maintained in blockchain only when a previous task allocation is successfully taken place. The proposed method is evaluated by experiments on Hyperledger Fabric, and results are reported. Our findings indicate that the proposed method enables the IoT environment to collect feedback values robustly. The technique can be applied in a blockchain-based trust management model for security enhancement.
Learning, forgetting, and the correlation of knowledge in knowledge space theory

In this work we introduce and study multiple properties and conditions related to the modeling of student knowledge in knowledge space theory (KST). We begin by looking at a property called forgetting consistency, which enforces a systematic way of forgetting within a knowledge structure. Next, we analyze in detail a concept we call positive knowledge correlation. This concept postulates that knowing more should not make it less likely that a student knows a particular concept. Among other things, we find that satisfying positive knowledge correlation implies the knowledge structure is closed under both union and intersection, and we also perform an empirical evaluation to assess the validity of the property. Finally, in the context of an adaptive assessment, we conclude with an analysis of the related concept of a positively correlated updating rule.
Motion Planning of Unmanned Aerial Vehicle Based on Rapid-exploration Random Tree Algorithm

Abstract In the study of route planning problems in complex environments, in order to reduce the flight cost of unmanned aerial vehicles (UAVs), it is necessary to achieve a better balance between planning time and path quality. This paper utilizes the Rapid-exploration Random Tree (RRT) algorithm for motion planning of a fixed-wing UAV and a multi-rotor UAV (i.e., a quad-rotor UAV), and gives the origin and destination locations on a 3-D map. By following aerodynamic constraints such as maximum roll angle, flight path angle, and airspeed, a collision-free and flight-friendly path is found through simulation. In addition, this paper employs a path smoothing algorithm to simplify the 3-D Dubins path and generates the shortest trajectory. The simulation results show that the RRT and the optimization method have faster convergence speed and shorter search time, reduce redundant planning points, shorten the planning track, and improve the track planning efficiency.
Optimized Quantum Phase Estimation for Simulating Electronic States in Various Energy Regimes

While quantum algorithms for simulations exhibit better asymptotic scaling than their classical counterparts, they currently cannot be accurately implemented on real-world devices. Instead, chemists and computer scientists rely on costly classical simulations of these quantum algorithms. In particular, the quantum phase estimation (QPE) algorithm is among several approaches that has attracted much attention in recent years due to its genuine quantum character. However, it is memory-intensive to simulate and intractable for moderate system sizes. This paper discusses the performance and applicability of QPESIM, a new simulation of the QPE algorithm designed to take advantage of modest computational resources. In particular, we demonstrate the versatility of QPESIM in simulating various electronic states by examining the ground and core-level states of H2O. For these states, we also discuss the effect of the active-space size on the quality of the calculated energies. For the high-energy core-level states, we demonstrate that new QPE simulations for active spaces defined by 15 active orbitals significantly reduce the errors in core-level excitation energies compared to earlier QPE simulations using smaller active spaces.
Exploring Security Procedures in Secure Software Engineering: A Systematic Mapping Study

Various new technologies have developed as software security solutions have become more critical. One of the essential parts of software quality is the product's security. Though providing examples covering all phases of secure software development is necessary, very few of these situations have been documented. More than a few approaches have been proposed and implemented to handle software security, but only a few of them provide valid evidence for developing secure software applications. This paper presents the results of a Systematic Mapping Study (SMS), which was carried out to determine the existence of software security metrics, tools, standards, and security-related research topics mainly discussed and addressed. A total of 116 studies were chosen for inclusion in this review. Selected studies led us to discover 55 Secure Software Engineering (SSE) metrics, 68 SSE tools, 33 SSE standards, and 12 SSE research topics that have been discussed and addressed. This effort will aid software development firms in better understanding existing security measures employed in creating secure software. It can also serve as a foundation for researchers to build and create new software security solutions and identify new research directions.
Simplified estimation modeling of land surface solar irradiation: A comparative study in Australia and China

Solar irradiation maps are fundamental geospatial datasets that have been used in a variety of research fields. However, it is difficult to estimate the continuous distribution of solar irradiation over large areas accurately by using conventional interpolation or extrapolation methods based on only a few observation stations. To tackle this problem, this study proposed a method to estimate spatially continuous land surface solar irradiation based on four machine learning models, namely, Gradient Boosting Machine (GBM), Random Forest (RF), Support Vector Regression (SVR), and Multilayer Perceptron (MLP). Clear-sky solar irradiation data were computed based on time and location, cloud optical thickness (COT) and aerosol optical thickness (AOT) that significantly influence solar irradiation were retrieved from Himawari-8 meteorological satellite images, and land surface solar irradiation data were obtained from observation stations for training and evaluation. To explore the weather effects on land surface solar irradiation, air temperatures, humidity, wind, and atmospheric pressure were also quantified and integrated into the models. As a comparative study, this study collected six-year historical data and estimated solar distribution at a 5-km spatial resolution in Australia and China. Based on the coefficient of determination (R2), normalized Root Mean Square Error (nRMSE), normalized mean bias error (nMBE), and consumption of time (t), the results show that GBM achieved the highest accuracy with R2 >0.7 at all stations, followed by RF, SVR, and MLP. It suggests that the proposed method can provide an accurate and reliable estimation of land surface solar irradiation, compared with the theoretical solar irradiation without the obstacle of the atmosphere. The annual solar distribution maps created by the built methods indicate that the proposed method is simple and effective for large geographical regions and can be used worldwide when similar datasets are obtained.
Using Decision Tree as Local Interpretable Model in Autoencoder-based LIME

Nowadays, deep neural networks are being used in many domains because of their high accuracy results. However, they are considered as “black box” means that they are not explainable for humans. On the other hand, in some tasks such as medical, economic, and self-driving cars, users want the model to be interpretable to decide if they can trust these results or not. In this work, we present a modified version of an autoencoder-based approach for local interpretability called ALIME. The ALIME itself is inspired by a famous method called Local Interpretable Model-agnostic Explanations (LIME). LIME generates a single instance level explanation by generating new data around the instance and training a local linear interpretable model. ALIME uses an autoencoder to weigh the new data around the sample. Nevertheless, the ALIME uses a linear model as the interpretable model to be trained locally, just like the LIME. This work proposes a new approach, which uses a decision tree instead of the linear model, as the interpretable model. We evaluate the proposed model in case of stability, local fidelity, and interpretability on different datasets. Compared to ALIME, the experiments show significant results on stability and local fidelity and improved results on interpretability.
The Degito-Financial Movement's Impact of Digital Transformation on Financial Services

The digital revolution and big data are improving the real-time analysis capacity of organizations, allowing them to optimize their tools and processes and to confront the available data with greater efficiency. In this context, where the time spent on data collection is decreasing, many functions in the company see their missions evolve towards more analysis and communication to help decision making. The COVID-19 pandemic has given a tremendous boost to the development of digitalization in the world. How then will the digitalization of financial services in Africa consolidate after this pandemic and what are its challenges?
Opening up the peer review process

Abstract Many science journals have begun either to provide authors with the opportunity to publish peer review reports alongside their published article or to use a form of interactive open access peer review, which means that the review process is made public from the start. However, because of the traditionally occluded nature of peer reviewing, much applied linguistics research on the topic has focused on corpora of individual reviews rather than the negotiation process between the author(s), reviewers, and editors that peer reviewing essentially entails. In this paper, the focus is on this negotiation process. The data are drawn from an open access journal in geosciences. They consist of review histories of three research papers with clusters of peer review reports, short comments, author replies, and editor decision letters. These clusters have been analysed chronologically in relation to one another, considering their impact and the evaluating authorities evoked in the process. The findings show that the brokers paid attention to both study- and text-related aspects in their evaluations, thus highlighting brokering as an activity related to both knowledge and text. Reviewer authority was recognised by the authors and editors alike, but the authors were also found to negotiate their divergent positions.
Go ahead and do not forget: Modular lifelong learning from event-based data

Lifelong learning is a long-standing aim for artificial agents that act in dynamic environments in which an agent needs to accumulate knowledge incrementally without forgetting previously learned representations. Contemporary methods for incremental learning from images are predominantly based on frame-based data recorded by conventional shutter cameras. We investigate methods for learning from data produced by event cameras and compare techniques to mitigate forgetting while learning incrementally. We propose a model that is composed of both, feature extraction and incremental learning. The feature extractor is utilized as a self-supervised sparse convolutional neural network that processes event-based data. The incremental learner uses a habituation-based method that works in tandem with other existing techniques. Our experimental results show that the combination of different existing techniques with our proposed habituation-based method can help avoid catastrophic forgetting even more, while learning incrementally from the features provided by the extraction module.
An Active Learning Algorithm Based on the Distribution Principle of Bhattacharyya Distance

Active learning is a method that can actively select examples with much information from a large number of unlabeled samples to query labeled by experts, so as to obtain a high-precision classifier with a small number of samples. Most of the current research uses the basic principles to optimize the classifier at each iteration, but the batch query with the largest amount of information in each round does not represent the overall distribution of the sample, that is, it may fall into partial optimization and ignore the whole, which will may affect or reduce its accuracy. In order to solve this problem, a special distance measurement method—Bhattacharyya Distance—is used in this paper. By using this distance and designing a new set of query decision logic, we can improve the accuracy of the model. Our method embodies the query of the samples with the most representative distribution and the largest amount of information to realize the classification task based on a small number of samples. We perform theoretical proofs and experimental analysis. Finally, we use different data sets and compare them with other classification algorithms to evaluate the performance and efficiency of our algorithm.
Dynamic distributed <scp>KC<sub><i>i</i></sub></scp>‐slice data publishing model with multiple sensitive attributes

SUMMARY Privacy‐preserving data publishing (PPDP) provides scope for performing various types of analytics by the researcher on the published data without revealing the individuals' privacy. However, it is necessary to consider the data from all the available locations to obtain accurate results from the published data. Then only the derived associations from the published data are helpful to make new decisions by an organization. Existing PPDP models on multiple sensitive attributes like KC ‐slice (K stands for bucket size, C stands for constant threshold), KC i ‐slice (K–bucket size, C i –variable threshold), Novel KC i ‐slice , and Optimal KC i ‐slice models are concentrated only when data is located at a single location. This research work introduces a new model called “ distributed KC i ‐slice ,” which considers the data with multiple sensitive attributes from various locations to obtain accurate results. Initially, this model proposes a novel technique called a distributed algorithm to merge the different data sets into a single data set. It combines the data differently, so that it is not possible to identify any record of a specific data set from the merged data. The proposed model also uses a new bucketization algorithm named (l,m,d) * ‐anonymity ( l stands for distinct semantic categories of the sensitive values of a sensitive attribute in a specific bucket, m stands for the number of sensitive attributes, and d stands for different sensitive categories for the sensitive values of a sensitive attribute in each sensitive bucket) for the bucketization of the tuples into the buckets. It imposes the privacy constraints only on high sensitive values of each sensitive attribute by considering the semantic and sensitive levels of sensitive values. The distributed KC i ‐slice model finally publishes the data in the form of multiple sensitive tables and one quasi table. This model is implemented based on attribute sensitiveness to enhance the utility and privacy levels of the published data.
Robust Multi-Label Classification with Enhanced Global and Local Label Correlation

Data representation is of significant importance in minimizing multi-label ambiguity. While most researchers intensively investigate label correlation, the research on enhancing model robustness is preliminary. Low-quality data is one of the main reasons that model robustness degrades. Aiming at the cases with noisy features and missing labels, we develop a novel method called robust global and local label correlation (RGLC). In this model, subspace learning reconstructs intrinsic latent features immune from feature noise. The manifold learning ensures that outputs obtained by matrix factorization are similar in the low-rank latent label if the latent features are similar. We examine the co-occurrence of global and local label correlation with the constructed latent features and the latent labels. Extensive experiments demonstrate that the classification performance with integrated information is statistically superior over a collection of state-of-the-art approaches across numerous domains. Additionally, the proposed model shows promising performance on multi-label when noisy features and missing labels occur, demonstrating the robustness of multi-label classification.
Evaluation of Multi-Scale Multiple Instance Learning to Improve Thyroid Cancer Classification

Thyroid cancer is currently the fifth most common malignancy diagnosed in women. Since differentiation of cancer sub-types is important for treatment and current, manual methods are time consuming and subjective, automatic computer-aided differentiation of cancer types is crucial. Manual differentiation of thyroid cancer is based on tissue sections, analysed by pathologists using histological features. Due to the enormous size of gigapixel whole slide images, holistic classification u sing deep learning methods is not feasible. Patch based multiple instance learning approaches, combined with aggre-gations such as bag-of-words, is a common approach. This work's contribution is to extend a patch based state-of-the-art method by generating and combining feature vectors of three different patch resolutions and analysing three distinct ways of combining them. The results showed improvements in one of the three multi-scale approaches, while the others led to decreased scores. This provides motivation for analysis and discussion of the individual approaches.
An Optimized Gaussian Extreme Learning Machine (GELM) for Predicting the Crop Yield using Soil Factors

Indian agriculture is extremely important and plays a predominant role in economy and employment. The agriculture has seen a significant technological transition because of data collection, environmental factors, crop selection, soil nutrients, pesticides and plant disease for making better farming decisions. This revolution in agriculture is addressed by using emerging technologies. Early detection and management of crop yield indicator problems can help to increase the yield and subsequent profit. Machine learning is an emerging technology used in agricultural research for yield prediction. To produce accurate results, a simplest and very fast optimized learning algorithm called GELM (Gaussian Extreme Learning Machine) classifier with different kinds of activation functions are used. For the soil dataset, the classifier is trained using 50 hidden neurons with different activation functions. The performance analysis of the system shows that gaussian extreme learning achieves an accuracy of 97% compared to other algorithms. This analysis helps in interpretation of results in efficient manner for any regional soil data.
Environmental geophysical study for ideal locations of landfill within Iraqi Southern Desert

Solid wastes and toxic chemicals are the main contributors, which influence the environment and quality of life. Demographic population, economic and building structures considered as major factors that are controlling such pollution. Therefore, it is very important to find proper areas to dispose of these pollutants. The current study aims to find best locations for wastes disposal areas that comply with the national and international standards. The study area, the Iraqi Southern Desert had been subjected to comprehensive study for geology, gravity and magnetic investigations. The existence of impermeable beds, tectonic, seismology and structural features show that the area understudy is located in the stable shelf that has no folding and faulting complexity; however, the saline groundwater lies at about 150 m depth. The gravity field data were analyzed using three methods: Trend Surface Analysis, Average Smooth Matrix and the First Vertical Derivative. Negative and positive gravity anomalies were noticed and interpreted. Remote sensing is also carried out in order to locate linear features, drainage patterns and watersheds within the Southern Desert depending on the interpretation of satellite images of the study area. Finally, a comparison of natural and environmental factors with remote sensing and both gravity and magnetic analyses gives thirteen locations of different coordinates. Only sites 1, 2, 3,10 and 13 yield good environment satisfaction agreement that permits us to choose them as a good locations for future landfill waste purposes.
Evolving clustering algorithm based on average cluster distance - eCAD

This paper presents a preliminary study of an evolving clustering algorithm based on the average cluster distance - eCAD. This algorithm is mainly intended for online processing of data streams. It recursively computes the average distance between two consecutive data samples, which is included as the main measure in the evolving mechanisms. Accordingly, we have included a mechanism to automatically detect new clusters and a mechanism to merge existing clusters. This makes the algorithm autonomous and insensitive to the distribution of the data, which does not need to be prescaled or normalized. In addition, the number of clusters is not required as a predefined parameter. This algorithm could be classified as a density-based clustering algorithm and we therefore provide some comparison results with established clustering techniques.