Reviewerly/exHarmony huggingface dataset:
Applied Methodological Approach for the Assessment of Soil Contamination by Trace Elements around Abandoned Coal Mines—A Case Study of the Jerada Coal Mine, Morocco

Previously conducted studies have established that soil contamination by trace elements (TEs) around coal mines affects ecological biodiversity and human health. One of the ways to protect the health of the surrounding population of coal mine sites is through the assessment of contamination impacts by accurate procedures for the establishment of relevant reclamation plan. The aim of the study is to develop an applied methodological approach to easily assess and quantify the impact of coal mining on the surrounding soils. To achieve this goal, a set of relevant geochemical indexes (enrichment factor—EF, geochemical background—GB, and geoaccumulation index—Igeo) and a geostatistical interpolation for mapping soil contamination were applied. It is experimentally established that the process of combining these techniques can be of great interest in highlighting the anomalous areas and revealing the contamination sources. A case study has been carried out on of the abandoned coal Jerada mine site in Morocco. Soils and coal mine waste rocks (CMWR) samples were collected from this site. Soil and CMWR samples were analyzed for their geochemical content. CMWRs were characterized further for their mineralogical composition and environmental behavior. Based on the EF, three TEs (As, Pb, and Zn) were targeted. The results of GB and Igeo calculations have established an evident relationship between the CMWR dump and the occurred soil contamination. TEs distribution pattern has been revealed using the obtained geochemical maps. The developed methodological approach may be in high demand when assessing soil contamination in most of abandoned coal mine sites around the word.
Comparing Efficiency and Performance of IoT BLE and RFID-Based Systems for Achieving Contract Tracing to Monitor Infection Spread among Hospital and Office Staff

COVID-19 is highly contagious and spreads rapidly; it can be transmitted through coughing or contact with virus-contaminated hands, surfaces, or objects. The virus spreads faster indoors and in crowded places; therefore, there is a huge demand for contact tracing applications in indoor environments, such as hospitals and offices, in order to measure personnel proximity while placing as little load on them as possible. Contact tracing is a vital step in controlling and restricting pandemic spread; however, traditional contact tracing is time-consuming, exhausting, and ineffective. As a result, more research and application of smart digital contact tracing is necessary. As the Internet of Things (IoT) and wearable sensor device studies have grown in popularity, this work has been based on the practicality and successful implementation of Bluetooth low energy (BLE) and radio frequency identification (RFID) IoT based wireless systems for achieving contact tracing. Our study presents autonomous, low-cost, long-battery-life wireless sensing systems for contact tracing applications in hospital/office environments; these systems are developed with off-the-shelf components and do not rely on end user participation in order to prevent any inconvenience. Performance evaluation of the two implemented systems is carried out under various real practical settings and scenarios; these two implemented centralised IoT contact tracing devices were tested and compared demonstrating their efficiency results.
An Artwork Rental System Based on Blockchain Technology

In recent years, due to the slowdown of the global economy and the instability of the stock market and real estate market, the art market has gradually become the third-largest investment market after the former two. As more and more funds flow into the art market, the two seemingly unrelated industries, art, and finance have increasingly close cooperation due to the growing prosperity of the art market. As a very important part of the process of art financialization, the development of art banks has also received extensive attention from all walks of life. In addition, blockchain, as a technology to jointly maintain reliable databases through trustlessness and decentralization, has grown rapidly in recent years, and is gradually applied in various fields. The advantages of blockchain technology are decentralization, anonymity, immutability, and traceability of stored information. The proposed scheme applies the characteristics of blockchain technology to Art Bank’s art rental system, and will have the following advantages: the use of Hyperledger technology, various art banks being merged into an alliance, and information among alliance members being shared, which is convenient for tenants to comprehensively query a single item. Using blockchain technology, in an environment where there is no central authority, under the premise of preventing the tampering of artwork-related information, ensures that the detailed information of the artwork is shared. Traditional leasing agreements can be compiled into smart contract leasing contracts to automatically run and manipulate data. The proposed protocol satisfies the following security requirements: identities’ mutual authentication, non-repudiation between every two parties, and other major security requirements based on blockchain. When a dispute arises, our proposed scheme also has an arbitration mechanism to clarify responsibilities.
Learning Correspondences of Cardiac Motion from Images Using Biomechanics-Informed Modeling

Learning spatial-temporal correspondences in cardiac motion from images is important for understanding the underlying dynamics of cardiac anatomical structures. Many methods explicitly impose smoothness constraints such as the $$\mathcal {L}_2$$ norm on the displacement vector field (DVF), while usually ignoring biomechanical feasibility in the transformation. Other geometric constraints either regularize specific regions of interest such as imposing incompressibility on the myocardium or introduce additional steps such as training a separate network-based regularizer on physically simulated datasets. In this work, we propose an explicit biomechanics-informed prior as regularization on the predicted DVF in modeling a more generic biomechanically plausible transformation within all cardiac structures without introducing additional training complexity. We validate our methods on two publicly available datasets in the context of 2D MRI data and perform extensive experiments to illustrate the effectiveness and robustness of our proposed methods compared to other competing regularization schemes. Our proposed methods better preserve biomechanical properties by visual assessment and show advantages in segmentation performance using quantitative evaluation metrics. The code is publicly available at https://github.com/Voldemort108X/bioinformed_reg .
Intelligent Pick-and-Place System Using MobileNet

The current development of a robotic arm solution for the manufacturing industry requires performing pick-and-place operations for work pieces varying in size, shape, and color across different stages of manufacturing processes. It aims to reduce or eliminate the human error and human intervention in order to save manpower costs and enhance safety at the workplace. Machine learning has become more and more prominent for object recognition in these pick-and-place applications with the aid of imaging devices and advances in the image processing hardware. One of the key tasks in object recognition is feature extraction and object classification based on convolutional neural network (CNN) models, which are generally computationally intensive. In this paper, an intelligent object detection and picking system based on MobileNet is developed and integrated into an educational six-axis robotic arm, which requires less computation resources. An experimental test is conducted on six-axis robotic arm called Niryo One to train the model and identify three objects with difference shapes and colors. It is shown by the confusion matrix that the MobileNet model achieves an accuracy of 91%, a dramatic improvement compared to 65% of the Niryo One’s original sequential model. The statistical study also shows the MobileNet can achieve a higher precision with more clustered spread of accuracy.
Dual-Channel Edge-Featured Graph Attention Networks for Aspect-Based Sentiment Analysis

The goal of aspect-based sentiment analysis (ABSA) is to identify the sentiment polarity of specific aspects in a context. Recently, graph neural networks have employed dependent tree syntactic information to assess the link between aspects and contextual words; nevertheless, most of this research has neglected phrases that are insensitive to syntactic analysis and the effect between various aspects in a sentence. In this paper, we propose a dual-channel edge-featured graph attention networks model (AS-EGAT), which builds an aspect syntactic graph by enhancing the contextual syntactic dependency representation of key aspect words and the mutual affective relationship between various aspects in the context and builds a semantic graph through the self-attention mechanism. We use the edge features as a significant factor to determine the weight coefficient of the attention mechanism to efficiently mine the edge features of the graph attention networks model (GAT). As a result, the model can connect important sentiment features of related aspects when dealing with aspects that lack obvious sentiment expressions, pay close attention to important word aspects when dealing with multiple-word aspects, and extract sentiment features from sentences that are not sensitive to syntactic dependency trees by looking at semantic features. Experimental results show that our proposed AS-EGAT model is superior to the current state-of-the-art baselines. Compared with the baseline models of LAP14, REST15, REST16, MAMS, T-shirt, and Television datasets, the accuracy of our AS-EGAT model increased by 0.76%, 0.29%, 0.05%, 0.15%, 0.22%, and 0.38%, respectively. The macro-f1 score increased by 1.16%, 1.16%, 1.23%, 0.37%, 0.53%, and 1.93% respectively.
Teaching Digital Competences in University Professors: A Meta-analysis and Systematic Literature Review in Web of Science

This article aims to analyze the scientific production from the Web of Science (WoS) database, associated to teaching digital competence (TDC) in universities. Based on the PRISMA methodology, the following research variables were taken into consideration: 1. Countries in which researches are carried out; 2. Researchers interested in the topic; 3. Methodological designs used in the investigations; 4. Objectives stated in the studies and 5. Main findings or contributions. The research was delimited through ERIC thesaurus or its approximations, 187 scientific publications were located. It is evident that, at the European level, especially in Spain, there is a significant number of authors who concentrate their interest in the subject, at the Latin American level there are isolated efforts and limited policies to develop digital teaching skills, so the status of study is germinal; research approaches are predominantly quantitative. It is discovered that the ambiguity caused by the complexity of conceptualization of the subject limits the possibility of establishing satisfactory protocols in the generation of training plans; through the research analyzed a significant gap is identified in some of the dimensions based on the different frames of reference. The research is established as the starting point for a similar analysis in other scientific databases, and it is noticed the need to define and develop TDC reference frameworks oriented to hybrid, online or distance study modalities.
Enriching user stories with usability features in a remote agile project: a case study

Considering usability aspects in the early phases of software development can avoid rework to solve usability problems identified late. We can adopt Requirements Engineering methods to identify usability requirements that explore the end users’ perspectives and usage preferences. However, usability evaluation methods are more commonly adopted in the industry than early usability methods to define usability requirements. Recently, the USARP method was proposed for usability requirements elicitation and specification. USARP suggests the combined use of personas, user stories, and usability guidelines. This paper aims to report a case study on the USARP adoption by a remote agile team in the industry. USARP supports the team in defining UI/UX requirements for a web management tool. A survey enables us to investigate the teams’ perception of the usefulness of the USARP. The results showed that USARP improved the team’s effectiveness in usability requirements elicitation. USARP enabled identifying 120 UI/UX aspects related to system functionalities. We expect this experience report can contribute to disseminating early usability methods in industry practice.
Design for IoT: An Experimental Study to Understand How Design Thinking Can Help in the Elicitation of Requirements for IoT Systems

Context: The Internet of Things (IoT) consists of networks of physical objects embedded with sensors, software, and other technologies to maintain communication and exchange data over a network. These characteristics bring new challenges to the entire development cycle, including requirements engineering (RE). RE practices have been adapted to meet market dynamics and ensure software quality, using a variety of approaches to support this process. One approach for RE is the use of design thinking (DT) techniques, as IoT systems are closely related to product and service innovation. Objective: To analyze the use of DT techniques in the context of IoT systems, evaluating the support for requirements elicitation. Method: An experimental study was carried out in the context of a practical requirements elicitation activity, aiming to obtain results on the support of DT techniques and their ease of use during requirements elicitation for IoT systems. Results: The statistical test did not show significant differences between the groups that used different sets of techniques. In both groups, participants were able to identify requirements related to the IoT paradigm, such as hardware integration, connectivity, mobility, sensing and actuation. The feedback analysis on the ease of use of the techniques pointed out similar difficulties reported by the participants of the two groups, mainly in relation to the terms present in the templates of the techniques. Conclusion: This article aims to contribute to RE in the IoT context, presenting quantitative data and on the ease of use of DT techniques during the requirements elicitation activity.
AFI-GAN: Improving feature interpolation of feature pyramid networks via adversarial training for object detection

Recent convolutional detectors learn strong semantic features by generating and combining multi-scale features via feature interpolation. However, simple interpolation incurs often noisy and blurred features. To resolve this, we propose a novel adversarially-trained interpolator which can substitute for the traditional interpolation effortlessly. In specific, we design AFI-GAN consisting of an AF interpolator and a feature patch discriminator. In addition, we present a progressive adversarial learning and AFI-GAN losses to generate multi-scale features for downstream detection tasks. However, we can also finetune the proposed AFI-GAN with the recent multi-scale detectors without the adversarial learning once a pre-trained AF interpolator is provided. We prove the effectiveness and flexibility of our AF interpolator, and achieve the better box and mask APs by 2.2% and 1.6% on average compared to using other interpolation. Moreover, we achieve an impressive detection score of 57.3% mAP on the MSCOCO dataset. Code is available at https://github.com/inhavl-shlee/AFI-GAN.
An iterative cyclic tri-strategy hybrid stochastic fractal with adaptive differential algorithm for global numerical optimization

Many real-life problems can be formulated as numerical optimization problems. Such problems pose a challenge for researchers when designing efficient techniques that are capable of finding the desired solution without suffering from premature convergence. This paper proposes a novel evolutionary algorithm that blends the exploitative and explorative merits of two main evolutionary algorithms, namely the Stochastic Fractal Search (SFS) and a Differential Evolution (DE) variant. This amalgam has an effective interaction and cooperation of an ensemble of diverse strategies to derive a single framework called Iterative Cyclic Tri-strategy with adaptive Differential Stochastic Fractal Evolutionary Algorithm (Ic3-aDSF-EA). The component algorithms cooperate and compete to enhance the quality of the generated solutions and complement each other. The iterative cycles in the proposed algorithm consist of three consecutive phases. The main idea behind the cyclic nature of Ic3-aDSF-EA is to gradually emphasize the work of the best-performing algorithm without ignoring the effects of the other inferior algorithm during the search process. The cooperation of component algorithms takes place at the end of each cycle for information sharing and the quality of solutions for the next cycle. The algorithm's performance is evaluated on 43 problems from three different benchmark suites. The paper also investigates the application to a set of real-life problems. The overall results show that the proposed Ic3-aDSF-EA has a propitious performance and a reliable scalability behavior compared to other state-of-the-art algorithms.
Addressing uncertainty in supply chain management through blockchain

Business organizations, nowadays, are facing more competitive and complex environment than ever before. To take the competitive advantage in the global supply chain, industries are now taking more risk and facing uncertainty in their supply chain. The blockchain is an emerging technology that plays a crucial role to bring transparency, security, and trust in all transactions among different entities of the supply chain such as manufacturing, storage, and distribution. In this context, this study addressing the uncertainty in the supply chain through blockchain technology. The study highlights the challenges, opportunities, and requirements of blockchain technology to handle the risk and uncertainty of the different supply chains. This study may be fruitful for various industries that are interested in adopting blockchain technology to tackle the risk and uncertainty in their supply chain.
Using case study analysis to develop heuristics to guide new filmmaking techniques in embodied virtual reality films

Filmmakers now have access to new cinematic pipelines that combine Virtual Reality (VR), allowing them to develop new immersive media for narrative and audience interaction. Traditional filmmaking approaches are limited when it comes to investigating the embodiment of VR and the underlying interaction between the user and the story in Embodied Virtual Reality (EVR) films. This study analyses four award-winning EVR films (Bonfire, The Line, The Key, and Wolves in the Walls) as case studies and proposes a set of heuristics to guide the production of new EVR films. The findings will be especially useful for independent filmmakers transitioning into EVR production, as well as the larger VR cinema community, in comprehending emergent VR storytelling.
Modelling penetration testing with reinforcement learning using capture‐the‐flag challenges: Trade‐offs between model‐free learning and a priori knowledge

Abstract Penetration testing is a security exercise aimed at assessing the security of a system by simulating attacks against it. So far, penetration testing has been carried out mainly by trained human attackers and its success critically depended on the available expertise. Automating this practice constitutes a non‐trivial problem because of the range and complexity of actions that a human expert may attempt. The authors focus their attention on simplified penetration testing problems expressed in the form of capture the flag hacking challenges, and analyse how model‐free reinforcement learning algorithms may help solving them. In modelling these capture the flag competitions as reinforcement learning problems the authors highlight the specific challenges that characterize penetration testing. The authors show how this challenge may be eased by relying on different forms of prior knowledge that may be provided to the agent. Since complexity scales exponentially as soon as the set of states and actions for the reinforcement learning agent is extended, the need to restrict the exploration space by using techniques to inject a priori knowledge is highlighted, thus making it possible to achieve solutions more efficiently.
DPPS: A deep-learning based point-light photometric stereo method for 3D reconstruction of metallic surfaces

Three-dimensional (3D) measurement provides essential geometric information for quality control and process monitoring in many manufacturing applications. Photometric stereo is one of the potential solutions for in-process metrology and active geometry compensation, which takes multiple images of an object under different illuminations as inputs and recovers its surface normal map based on a reflectance model. Deep learning approaches have shown their potential in solving the highly nonlinear problem for photometric stereo, but the main challenge preventing their practical application in process metrology lies in the difficulties in the generation of a comprehensive dataset for training the deep learning model. This paper presents a new Deep-learning based Point-light Photometric Stereo method, DPPS, which utilizes a multi-channel deep convolutional neural network (CNN) to achieve end-to-end prediction for both the surface normal and height maps in a semi-calibrated fashion. The key contribution is a new dataset generation method combining both physics-based and data-driven approaches, which minimizes the training cost and enables DPPS to handle reflective metal surfaces with unknown surface roughness. Even trained only with fully synthetic and high-fidelity dataset, our DPPS surpasses the state-of-the-art with an accuracy better than 0.15 cm over a 10 cm × 10 cm area and its real-life experimental results are on par with commercial 3D scanners. The demonstrated results provide guidance on improving the generalizability and robustness of deep-learning based computer vision metrology with minimized training cost as well as show the potential for in-process 3D metrology in advanced manufacturing processes.
Learning Discriminative Representation for Few-Shot Knowledge Graph Completion

Few-Shot Knowledge Graph Completion (FKGC) aims to infer the missing facts of a relation with only few corresponding known instances. To accomplish this task, one prominent prerequisite is to fully explore and exploit useful information from these supporting instances. However, many existing methods usually treat the support set as a group of independent contents, and fail to explicitly model the interactions among instances. They also majorly focus on positive instances in the support set while overlooking negative ones. Generally, much useful information in the support set has not been well applied yet. Therefore, this work proposes a novel model FSDR to fully capture the fertile information within supporting sets. Extensive experiments on two benchmark datasets demonstrate our model is indeed effective and outperforms state-of-the-art FKGC approaches.