Reviewerly/exHarmony huggingface dataset:
A deep neural network model for coreference resolution in geological domain

Coreference resolution of geological entities is an important task in geological information mining. Although the existing generic coreference resolution models can handle geological texts, a dramatic decline in their performance can occur without sufficient domain knowledge. Due to the high diversity of geological terminology, coreference is intricately governed by the semantic and expressive structure of geological terms. In this paper, a framework CorefRoCNN based on RoBERTa and convolutional neural network (CNN) for end-to-end coreference resolution of geological entities is proposed. Firstly, the fine-tuned RoBERTa language model is used to transform words into dynamic vector representations with contextual semantic information. Second, a CNN-based multi-scale structure feature extraction module for geological terms is designed to capture the invariance of geological terms in length, internal structure, and distribution. Thirdly, we incorporate the structural feature and word embedding for further determinations of coreference relations. In addition, attention mechanisms are used to improve the ability of the model to capture valid information in geological texts with long sentence lengths. To validate the effectiveness of the model, we compared it with several state-of-the-art models on the constructed dataset. The results show that our model has the optimal performance with an average F1 value of 79.78%, which is a 1.22% improvement compared to the second-ranked method.
Establishment of a trusted environment for IoT service provisioning based on X3DH-Based brokering and Federated Blockchain

The adoption of non-certified Internet of Things (IoT) devices can expose the system to cyber attacks that can disrupt IoT-based applications or generate fake data. At the same time, complex cryptographic approaches cannot be adopted due to the limited computational and power resources of IoT devices. In the literature, the certification of IoT devices is performed through a Certification Authority (CA) that generates and stores certificates for all the IoT nodes. Usually, CA is hosted in remote sites (e.g., in the Cloud or the IoT service administrator's private network) and this exposes the IoT ecosystem to attacks. This paper overcomes these challenges by proposing a new Broker based certification process which decouples at the Edge the communication between IoT devices and the CA. Acting as an “intermediary”, the Mobile Edge Computing (MEC) node shields the communication between untrusted IoT devices and the CA, taking the responsibility for the node certification. The establishment of a trusted ecosystem is further reinforced to guarantee integrity and non-repudiation of the data by using a Federated Blockchain, which is a distributed storage of non-falsifiable data in digital ledgers. Confidentiality and robustness against network issues or temporary disconnections is also achieved using the Extended Triple Diffie-Hellman (X3DH) protocol, which set up secure communication over the Internet among the involved. In the paper, we present the design of the whole proposed solution together with the exploited technologies and details on our implementation. We also present evaluation results to show the efficiency and performance of our solution.
Self-Efficacy in Online Teaching during the Immediate Transition from Conventional to Online Teaching in German and Argentinian Universities—The Relevance of Institutional Support and Individual Characteristics

Triggered by the spread of the Coronavirus and the lockdown of universities in spring 2020, universities were required to provide infrastructure for digital teaching within a very short time. Further, all university members needed to develop knowledge and skills for teaching online. This paper presents data from the cross-cultural CRTS-Study (Coronavirus-Related Teaching Situation Study), which compares the experiences, attitudes and needs of university teachers in Germany and Argentina during the first lockdown in the context of the Coronavirus pandemic. The study has been carried out in spring 2020 as a cross-sectional online survey study with university teachers in Germany and Argentina (N = 728). The overall picture reveals a mostly successful implementation of online teaching for university teachers in both countries, with Argentinian university teachers reporting a slightly more positive perspective and slightly higher self-efficacy beliefs in online teaching when compared with the German colleagues. The results of regression analysis hint at the relevance of prior personal experience and institutional support for self-efficacy beliefs in online teaching for both samples. In conclusion, individual experience and training as well as supportive institutional conditions seem to be relevant for the development of digital teaching at universities in both countries.
Perturbation Observer-Based Obstacle Detection and Its Avoidance Using Artificial Potential Field in the Unstructured Environment

Different methodologies for manipulators have been proposed and applied to robot obstacle detection and avoidance in unstructured environments. These methods include different real-time sensors, observer-based algorithms, and path planning using genetic algorithms. However, sensor design integration is complex and considerably expensive. Moreover, the observer algorithm requires complete system dynamics information, which is difficult to derive. In this regard, genetic algorithms are typically considered slow and difficult to optimize. Accordingly, this study proposes a sensor-less obstacle detection technique using a nonlinear observer (known as sliding perturbation observer (SPO)). Obstacle avoidance is also implemented using a motion planner (known as artificial potential field (APF)). The SPO is a nonlinear observer that only requires the partial position and provides all other states (such as position, velocity) and perturbation (non-linearities and external disturbance). The SPO estimates the external torque at each joint resulting from contact (i.e., collision) with an obstacle. Obstacles are detected and avoided by integrating the SPO and APF. The estimated external torque detects the obstacle location and a repulsive force from the APF is applied to avoid this obstacle. To achieve obstacle avoidance, the sum of all estimated torques must be zero. The proposed technique is applied to a robot manipulator with five degrees of freedom.
Contextual structure as an approach to the study of virtual reality learning environment

This study aims to offer a contextual framework for a virtual reality learning environment (VRLE) that would assist in interpreting students' and teachers' expectations on how to use VR in the learning process. Due to the current lack of unified recommendations and principles, as well as framework methods, the structure of VRLE, consisting of 4, pedagogical, technological, social and health-preservation contexts may be one approach to the study of VR in education. The study involved 53 school students aged 10–16 years and 49 teachers of various subjects at schools. The participants' opinions were assessed after the immersion into the VR. The results showed that 79,2 % of learners agreed to learn in school using VR, and 77,5 % of teachers expect to use it in the next five years. A quarter of the learners believe that VR promotes independent and comfortable learning outside the classroom. However, half of the participants are concerned about the possible undesirable health effects of VR. The prospects for further research lie in defining the criteria and designing VR models for each context including technological, pedagogical, social, and health-preservation contexts.
Smartphone sensors‐based human activity recognition using feature selection and deep decision fusion

Abstract Human activity recognition (HAR) with smartphone sensors is a significant research direction in human‐cyber‐physical systems. Aiming at the problem of feature redundancy and low recognition accuracy of HAR, this paper presents a novel system architecture comprising three parts: feature selection based on an oppositional and chaos particle swarm optimization (OCPSO) algorithm, multi‐input one‐dimensional convolutional neural network (MI‐1D‐CNN) utilizing time‐domain and frequency‐domain signals, and deep decision fusion (DDF) combining D‐S evidence theory and entropy. The proposed architecture is evaluated on the UCI HAR and WIDSM datasets. The results highlight that OCPSO performs better than particle swarm optimization (PSO) in feature selection, convergence speed, and recognition accuracy. Moreover, it is shown that for the MI‐1D‐CNN classifier, the frequency‐domain signals (95.96%) perform better than time‐domain signals (95.66%). In addition, this paper investigates the impact of the convolution layers, feature maps, filter sizes, and decision fusion methods on recognition accuracy. The results demonstrate that the DDF method (97.81%) outperforms single‐layer decision fusion in improving the recognition accuracy on the UCI HAR dataset.
DynamicRetriever: A Pre-trained Model-based IR System Without an Explicit Index

Web search provides a promising way for people to obtain information and has been extensively studied. With the surge of deep learning and large-scale pre-training techniques, various neural information retrieval models are proposed, and they have demonstrated the power for improving search (especially, the ranking) quality. All these existing search methods follow a common paradigm, i.e., index-retrieve-rerank, where they first build an index of all documents based on document terms (i.e., sparse inverted index) or representation vectors (i.e., dense vector index), then retrieve and rerank retrieved documents based on the similarity between the query and documents via ranking models. In this paper, we explore a new paradigm of information retrieval without an explicit index but only with a pre-trained model. Instead, all of the knowledge of the documents is encoded into model parameters, which can be regarded as a differentiable indexer and optimized in an end-to-end manner. Specifically, we propose a pre-trained model-based information retrieval (IR) system called DynamicRetriever, which directly returns document identifiers for a given query. Under such a framework, we implement two variants to explore how to train the model from scratch and how to combine the advantages of dense retrieval models. Compared with existing search methods, the model-based IR system parameterizes the traditional static index with a pre-training model, which converts the document semantic mapping into a dynamic and updatable process. Extensive experiments conducted on the public search benchmark Microsoft machine reading comprehension (MS MARCO) verify the effectiveness and potential of our proposed new paradigm for information retrieval.
Reputation-based Decentralized Autonomous Organization for the non-profit sector: Leveraging blockchain to enhance good governance

The Decentralized Autonomous Organization (DAO), a group organized by governance rules programmed on a blockchain, has recently been attracting attention as a novel organizational form. The effectiveness of a DAO’s decentralized governance mechanism and transparency, as secured by its code, has generally been discussed in contrast with traditional stock companies. However, the potential of a DAO for non-profits, which provide goods and services that profit-seeking organizations do not offer, has been less discussed. This paper presents a proof-of-concept implementation to demonstrate the advantages of utilizing a DAO governance framework for non-profits. To this end, this study developed a DAO governance framework incorporating a reputation-based decision-making system, a peer evaluation system, and a transparent, real-time accounting system for the Ethereum blockchain. Most current decentralized governance systems rely heavily on token-based voting using governance tokens with stock-like features. However, there is a need for a voting mechanism beyond token-based voting for non-profits, which do not have owners. Therefore, the developed application applies an existing reputation-based voting mechanism and integrates additional features, such as a membership system with mutual evaluation and a reputation NFT to visualize contributions. Several exemplar demonstrations were conducted to evaluate its key functionalities. This application enabled discussions across the boundary between technology and society in terms of the key aspects of non-profits: i) transparency of finance and governance, ii) participatory governance by diverse stakeholders, and iii) equity and inclusiveness of the consensus mechanism. The results indicated that blockchain technology compensates for a non-profit’s vulnerabilities, and illustrated that the proposed reputation-based governance mechanisms are well-motivated. However, the results also revealed that blockchain-based governance involves as many potential risks and limitations as it brings benefits. Lastly, by providing several possible solutions to these constraints as well as recommendations for future research, this paper contributes to the sustainable development of non-profits as one of the foundations of democratic governance.
Formation of Emotional Security of Students during the Period of Training in Conditions of Military Conflict

Ukrainian teachers and students are now in an extreme situation of military conflict when the territory of the country is under constant air fire, and the line of hostilities stretches for 1500 kilometres. The purpose of the article is to analyze the extreme learning conditions of Ukrainian students and develop a strategy for optimal emotional relaxation by selecting fiction for reading and deepening artistic perception. Respondents’ data (120 students and 53 pupils) were collected, systematized, evaluated, and analyzed using systematic written surveys (Project Tuning methodology) and statistical analysis methods. Testing corresponded to three stages of conflict development: conflict deployment, escalation, and post-conflict phase. Pedagogical activity mirrors this parable but in the opposite direction. Fiction thematically and genre-wise at each stage plays the role of a protective shield, which draws the line between students’ own emotions and the feelings of literary heroes. Perception of artistic texts was aimed at 1) identifying visual, auditory, and tactile associations; 2) olfactory sensations; 3) the ability to build associative chains; 4) imagining literary heroes; 5) emotionally immersing yourself in the world of fiction. Comparison of one’s own emotional perceptions with those that caused anti-utopian works and fantasy literature prove the effectiveness of the chosen pedagogical hypothesis.
On predicting school dropouts in Egypt: A machine learning approach

Abstract Compulsory school-dropout is a serious problem affecting not only the education systems, but also the developmental progress of any country as a whole. Identifying the risk of dropping out, and characterizing its main determinants, could help the decision-makers to draw eradicating policies for this persisting problem and reducing its social and economic negativities over time. Based on a substantially imbalanced Egyptian survey dataset, this paper aims to develop a Logistic classifier capable of early predicting students at-risk of dropping out. Training any classifier with an imbalanced dataset, usually weaken its performance especially when it comes to false negative classification. Due to this fact, an extensive comparative analysis is conducted to investigate a variety of resampling techniques. More specifically, based on eight under-sampling techniques and four over-sampling ones, and their mutually exclusive mixed pairs, forty-five resampling experiments on the dataset are conducted to build the best possible Logistic classifier. The main contribution of this paper is to provide an explicit predictive model for school dropouts in Egypt which could be employed for identifying vulnerable students who are continuously feeding this chronic problem. The key factors of vulnerability the suggested classifier identified are student chronic diseases, co-educational, parents' illiteracy, educational performance, and teacher caring. These factors are matching with those found by many of the research previously conducted in similar countries. Accordingly, educational authorities could confidently monitor these factors and tailor suitable actions for early intervention.
Active visual search in naturalistic environments reflects individual differences in classic visual search performance

Abstract Visual search is a ubiquitous activity in real-world environments. Yet, traditionally, visual search is investigated in tightly controlled paradigms, where head-restricted participants locate a minimalistic target in a cluttered array that is presented on a computer screen. Do traditional visual search tasks predict performance in naturalistic settings, where participants actively explore complex, real-world scenes? Here, we leverage advances in virtual reality technology to test the degree to which classic and naturalistic search are limited by a common factor, set size, and the degree to which individual differences in classic search behavior predict naturalistic search behavior in a large sample of individuals (N = 75). In a naturalistic search task, participants looked for an object within their environment via a combination of head-turns and eye-movements using a head-mounted display. Then, in a classic search task, participants searched for a target within a simple array of colored letters using only eye-movements. In each task, we found that participants’ search performance was impacted by increases in set size—the number of items in the visual display. Critically, we observed that participants’ efficiency in classic search tasks—the degree to which set size slowed performance—indeed predicted efficiency in real-world scenes. These results demonstrate that classic, computer-based visual search tasks are excellent models of active, real-world search behavior.
Novel Intensity Mapping Functions: Weighted Histogram Averaging

It is challenging to align the brightness distribution of the images with different exposures due to possible color distortion and loss of details in the brightest and darkest regions of input images. In this paper, a novel intensity mapping algorithm is first proposed by introducing a new concept of weighted histogram averaging (WHA). The proposed WHA algorithm leverages the correspondence between the histogram bins of two images which are built up by using the non-decreasing property of the intensity mapping functions (IMFs). Extensive experiments indicate that the proposed WHA algorithm significantly surpasses the related state-of-the-art intensity mapping methods.
Path Re-Planning Design of a Cobot in a Dynamic Environment Based on Current Obstacle Configuration

This study proposes a path planning algorithm to generate a collision-free path that avoids static and dynamic obstacles in real time. An efficient path re-planning method is presented for obstacle avoidance for a cobot in an environment that is shared by humans and robot. Static and dynamic obstacles are tracked when the manipulator executes a trajectory along a planned initial static path. When a dynamic obstacle enters the robot's workspace, the proposed method re-plans a collision-free local path to avoid static and dynamic obstacles. To allow fast local re-planning, a hybrid method that combines the advantages of APF and RRT path planning algorithm is proposed. The weight factors for the hybrid method is determined according to the current configuration of obstacles. The experimental results for a TM5-700 manipulator show that the proposed method decreases re-planning time and path length in an environment with static and dynamic obstacles. The path re-planning time is at least 55% less than those for two existent path planning optimization methods D-RRT and VF-RRT.
Where do people look when walking up and down familiar staircases?

Many activities in daily life do not impose strict requirements on gaze. We investigated gaze when walking up and down staircases within one's own house. We anticipated that using a variety of staircases in different environments and not informing participants that stair climbing was the focus of investigation might provide a description of gaze behavior that is closer to that used in our daily life than doing so under circumstances in which the focus is explicitly and exclusively directed at the stairs. We analyzed several measures, including the order in which participants fixated the steps. We confirmed that people often look at the steps sequentially, but found that they often made fixations back to steps they had already fixated. They also regularly skipped looking at several steps to fixate further ahead. On average, they directed their gaze at about half the steps. They looked further ahead when ascending than when descending staircases. Overall, the results are similar to those found under highly constrained laboratory conditions, although we do report some differences. One such difference is a tendency to fixate fewer steps. Another is that participants fixated steps that were less far ahead when descending staircases. We also introduced some new analyses that may help understand gaze behavior during stair climbing.
Emotional Variance Analysis: A new sentiment analysis feature set for Artificial Intelligence and Machine Learning applications

Sentiment Analysis (SA) is a category of data mining techniques that extract latent representations of affective states within textual corpuses. This has wide ranging applications from online reviews to capturing mental states. In this paper, we present a novel SA feature set; Emotional Variance Analysis (EVA), which captures patterns of emotional instability. Applying EVA on student journals garnered from an Experiential Learning (EL) course, we find that EVA is useful for profiling variations in sentiment polarity and intensity, which in turn can predict academic performance. As a feature set, EVA is compatible with a wide variety of Artificial Intelligence (AI) and Machine Learning (ML) applications. Although evaluated on education data, we foresee EVA to be useful in mental health profiling and consumer behaviour applications. EVA is available at https://qr.page/g/5jQ8DQmWQT4. Our results show that EVA was able to achieve an overall accuracy of 88.7% and outperform NLP (76.0%) and SentimentR (58.0%) features by 15.8% and 51.7% respectively when predicting student experiential learning grade scores through a Multi-Layer Perceptron (MLP) ML model.
Cases of application of blockchain on the supply chain: a literature review

Blockchain emerged as a leading technology layer for financial applications. Nevertheless, in the past years, the attention of researchers and practitioners moved to the application of the blockchain technologies in other domains. Supply chain is the topic playing more attention to the blockchain. In line with this consideration, this paper focuses on case studies of blockchain implementation targeting the supply chain context. Through a systematic literature review, we derived a list of 107 papers retrieved from the Scopus database and published in the main academic reference journals. Using these studies, this paper tracks and analyses the evolution of the research field based on publication year and journals, most prominent countries and authors and keywords’ analysis. The results provide a clear picture of the existing literature to identify the research gap of the current scientific research and to propose a future research agenda.