Reviewerly/exHarmony huggingface dataset:
Personalized and Explainable Employee Training Course Recommendations: A Bayesian Variational Approach

As a major component of strategic talent management, learning and development (L&amp;D) aims at improving the individual and organization performances through planning tailored training for employees to increase and improve their skills and knowledge. While many companies have developed the learning management systems (LMSs) for facilitating the online training of employees, a long-standing important issue is how to achieve personalized training recommendations with the consideration of their needs for future career development. To this end, in this article, we present a focused study on the explainable personalized online course recommender system for enhancing employee training and development. Specifically, we first propose a novel end-to-end hierarchical framework, namely Demand-aware Collaborative Bayesian Variational Network (DCBVN), to jointly model both the employees’ current competencies and their career development preferences in an explainable way. In DCBVN, we first extract the latent interpretable representations of the employees’ competencies from their skill profiles with autoencoding variational inference based topic modeling. Then, we develop an effective demand recognition mechanism for learning the personal demands of career development for employees. In particular, all the above processes are integrated into a unified Bayesian inference view for obtaining both accurate and explainable recommendations. Furthermore, for handling the employees with sparse or missing skill profiles, we develop an improved version of DCBVN, called the Demand-aware Collaborative Competency Attentive Network (DCCAN) framework , by considering the connectivity among employees. In DCCAN, we first build two employee competency graphs from learning and working aspects. Then, we design a graph-attentive network and a multi-head integration mechanism to infer one’s competency information from her neighborhood employees. Finally, we can generate explainable recommendation results based on the competency representations. Extensive experimental results on real-world data clearly demonstrate the effectiveness and the interpretability of both of our frameworks, as well as their robustness on sparse and cold-start scenarios.
Wearable technology for hazardous remote environments: Smart shirt and Rugged IoT network for forestry worker health

This paper introduces the architecture and details of our wearable IoT solution for workplace health and safety in rugged outdoor environments. We focus on the specific requirements defined by the New Zealand forestry environment as it is the industry with the most fatalities and highest accident rate in New Zealand. Neither consumer-level products nor professional wearables have been found to be suitable for forestry conditions. Furthermore, forestry workplaces cannot rely on existing networking infrastructure, and due to their remote and rugged nature, no permanent setup is possible. We describe the Hakituri project in which we integrate wearable devices, networking, data analytics, and a buddy system into a novel monitoring solution for worker health and safety. Our wearable IoT solution includes a dynamic network set-up, wearables suitable for forestry conditions, contextual rule-based data analytics, and real-time alerts. A wearable smart shirt carries sensors for collecting personal data from forestry workers, while further environmental sensors capture contextual data. The IoT network is set up for communication in unreliable and rugged outdoor environments. We explore a variety of setups to achieve the best quality outcomes for health and safety in the forestry domain.
Phonetic posteriorgram-based voice conversion system to improve speech intelligibility of dysarthric patients

Most dysarthric patients encounter communication problems due to unintelligible speech. Currently, there are many voice-driven systems aimed at improving their speech intelligibility; however, the intelligibility performance of these systems are affected by challenging application conditions (e.g., time variance of patient's speech and background noise). To alleviate these problems, we proposed a dysarthria voice conversion (DVC) system for dysarthric patients and investigated the benefits under challenging application conditions.A deep learning-based voice conversion system with phonetic posteriorgram (PPG) features, called the DVC-PPG system, was proposed in this study. An objective-evaluation metric of Google automatic speech recognition (Google ASR) system and a listening test were used to demonstrate the speech intelligibility benefits of DVC-PPG under quiet and noisy test conditions; besides, the well-known voice conversion system using mel-spectrogram, DVC-Mels, was used for comparison to verify the benefits of the proposed DVC-PPG system.The objective-evaluation metric of Google ASR showed the average accuracy of two subjects in the duplicate and outside test conditions while the DVC-PPG system provided higher speech recognitions rate (83.2% and 67.5%) than dysarthric speech (36.5% and 26.9%) and DVC-Mels (52.9% and 33.8%) under quiet conditions. However, the DVC-PPG system provided more stable performance than the DVC-Mels under noisy test conditions. In addition, the results of the listening test showed that the speech-intelligibility performance of DVC-PPG was better than those obtained via the dysarthria speech and DVC-Mels under the duplicate and outside conditions, respectively.The objective-evaluation metric and listening test results showed that the recognition rate of the proposed DVC-PPG system was significantly higher than those obtained via the original dysarthric speech and DVC-Mels system. Therefore, it can be inferred from our study that the DVC-PPG system can improve the ability of dysarthric patients to communicate with people under challenging application conditions.
The Effects of Topic Familiarity on Text Quality, Complexity, Accuracy, and Fluency: A Conceptual Replication

Abstract Understanding the effects of second language writers’ topic familiarity on their subsequent writing performance has been a subject of great interest to researchers in second language acquisition and TESOL. However, prior studies have tended to suffer from multiple methodological limitations (e.g., no interrater reliability, limited or no control over important variables), which may call into question their results. In this conceptual replication, we highlight some of these issues by revisiting an influential study by He and Shi (2012), which explored the effects of ESL students’ topic familiarity on their writing performance as assessed by measures of text quality and complexity, accuracy, and fluency (CAF). Apart from highlighting the differences in our studies’ designs (e.g., the CAF measures and statistical analyses used), we highlight the similarities and differences in our findings. Finally, we close with a call for increased replication research in this area.
In Search of the Essence of Low-Code: An Exploratory Study of Seven Development Platforms

Rapidly growing attention has been directed in recent years toward a type of software development and execution environment now passing under the name of ‘low-code development platforms.’ The fundamental claim is that limiting traditional coding mechanisms in favor of a variety of alternative means of design and specification yields substantial efficiency gains in professional and private software development. But although much stir at present surrounds low-code development platforms, it is by no means clear what, if any, features are distinctive of these systems, and whether any of these features mark out a technology which can be considered original. This paper presents an exploratory study of seven low-code development platforms, with the aim of discovering their essence and assessing them critically in the light of research in information systems development. An analysis framework covering a number of criteria regarding professional information systems development is used to characterize the selected platforms, and to point out features commonly, occasionally, and rarely possessed by them. The study reveals that hardly any features of low-code development are innovative in and of themselves, with novelty primarily consisting in their combination and integration. Still, we argue in conclusion, a number of research opportunities can be made out with an eye on the leitmotif of low-code development.
Analysis and Intellectual Structure of the Multi-Factor Authentication in Information Security

This study presents the current state of research on multi-factor authentication. Authentication is one of the important traits in the security domain as it ensures that legitimate users have access to the secure resource. Attacks on authentication occur even before digital access is given, but it becomes quite challenging with remote access to secure resources. With increasing threats to single authentication schemes, 2Factor and later multi-factor authentication approaches came into practice. Several studies have been done in the multi-factor authentication discipline, and most of them proposed the best possible approaches, but there are very limited studies in the area that can comprehend all these innovative and effective approaches. Using Web of Science data of the research publications on the topic, the study adopted the bibliometric approach to find the evolution of authentication in the security domain, especially multi-factor authentication. This study finds the impact of the research in the selected domain using bibliometric analysis. This research also identifies the key research trends that most of the researchers are paying attention to. The highest number of publications on multi-factor authentication were published in 2019 while the highest number of citations were received in 2014. United States, India, and China are the leading countries publishing the most on multi-factor authentication.
Earthquake Early Warning Systems in Taiwan: Current Status

The earthquake early warning (EEW) system in Taiwan is the outcome of rigorous research work carried out at various levels after the occurrence of the 1986 Hualien earthquake that caused destruction. After more than 25 years of development, three different EEW systems exist in Taiwan. Currently, the nation wide regional EEW system is operated by the Central Weather Bureau (CWB), whereas, a hybrid (regional & onsite) system based on Micro-Electro-Mechanical System sensors is run by National Taiwan University (NTU). The third EEW (onsite system) is run by the National Center for Research on Earthquake Engineering (NCREE). Both CWB and NTU systems are capable of reporting the EEW warnings within 20 seconds of earthquake occurrence. The CWB system is incharge of providing earthquake alerts in Taiwan via text message through mobile phone, TV, and directly broadcasting system to schools and is providing earthquake alarms to the general public since 2016. During recently damaging earthquakes in Taiwan, the NTU system provided 2–8 seconds onsite warning (lead time) in the blind zone around the epicenter. The NTU system also can generate near real-time shake maps for rapid response purposes. The NCREE system consists of about 98 stations and can provide several seconds lead time in the area near the epicenter. The NCREE system also can receive CWB regional EEW messages for warning the regions away from the epicenter. Individually every system has its advantage, however, the hybrid approach will be one of the future systems for real operation.
Quality measurement in agile and rapid software development: A systematic mapping

In despite of agile and rapid software development (ARSD) being researched and applied extensively, managing quality requirements (QRs) are still challenging. As ARSD processes produce a large amount of data, measurement has become a strategy to facilitate QR management. This study aims to survey the literature related to QR management through metrics in ARSD, focusing on: bibliometrics, QR metrics, and quality-related indicators used in quality management. The study design includes the definition of research questions, selection criteria, and snowballing as search strategy. We selected 61 primary studies (2001–2019). Despite a large body of knowledge and standards, there is no consensus regarding QR measurement. Terminology is varying as are the measuring models. However, seemingly different measurement models do contain similarities. The industrial relevance of the primary studies shows that practitioners have a need to improve quality measurement. Our collection of measures and data sources can serve as a starting point for practitioners to include quality measurement into their decision-making processes. Researchers could benefit from the identified similarities to start building a common framework for quality measurement. In addition, this could help researchers identify what quality aspects need more focus, e.g., security and usability that have surprisingly few metrics reported.
Linked open data in authoring virtual exhibitions

• The proposal of an engaging approach to exploit cultural heritage data without any technical skill. • A virtual exhibition generator that moves cultural heritage lovers in the role of exhibition curators. • A generator of virtual exhibitions exploiting linked open data. • A natural language interface to exploit linked open data in creating virtual exhibitions. • A unified approach to exploit both open data and linked open data in generating virtual exhibitions. In the last years, virtual exhibitions have been widely adopted to enhance traditional museums and enable active interaction with culture without posing any physical constraints. Nevertheless, people interested in cultural heritage still behave as visitors. To fully engage them, we propose to let cultural heritage lovers play the role of exhibition curators. In authoring virtual exhibitions, users have to perform a data selection phase that poses several challenges, including finding data sources and extracting data of interest. We aim to take advantage of data published as Knowledge Graphs in the Linked Open Data format. Users can query geographically distributed artworks thanks to their linking nature, manipulate heterogeneous data, and easily customise their exhibitions by exploiting the wide range of available cultural heritage knowledge graphs. However, the complexity of linked open data query languages (such as SPARQL) threatens their exploitation. Consequently, we need to mask SPARQL technical challenges and guide users in naturally posing questions to unlock the potentialities of linked open data to a broader audience. We propose a virtual exhibition authoring tool that guides users from knowledge graphs querying to the automatic generation of virtual experiences. The Knowledge Graph query phase relies on ELODIE, a natural language interface to scaffold users in retrieving data of interest without asking for technical skills in query languages. We introduce our prototype by describing its operating mechanism and by detailing its components. We present a Van Gogh’s experience as a use case by collecting all the artist’s artworks published on DBpedia (a well-known and general purpose knowledge graph) and organise them in a virtual reality-based virtual exhibition. Finally, we conclude by overviewing advantages and technical challenges posed by linked open data in designing and developing knowledge graph exploitation tools.
ME‐MADDPG: An efficient learning‐based motion planning method for multiple agents in complex environments

Developing efficient motion policies for multiagents is a challenge in a decentralized dynamic situation, where each agent plans its own paths without knowing the policies of the other agents involved. This paper presents an efficient learning-based motion planning method for multiagent systems. It adopts the framework of multiagent deep deterministic policy gradient (MADDPG) to directly map partially observed information to motion commands for multiple agents. To improve the efficiency of MADDPG in sample utilization, so as to train more brilliant agents that can adapt to more complex environments, a strategy named mixed experience (ME) is introduced to MADDPG, and this has led to our proposed ME-MADDPG algorithm. The novel ME strategy can be embodied into three specific mechanisms: (1) an artificial potential field-based sample generator to produce high-quality samples in the early training stage; (2) a dynamic mixed sampling strategy to mix the training data from different sources with a variable proportion; (3) a delayed learning skill to stabilize the training of the multiple agents. A series of experiments have been conducted to verify the performance of the proposed ME-MADDPG algorithm, and it has been demonstrated that, compared with MADDPG, the proposed algorithm can significantly improve the convergence speed and convergence effect in the training process, and it has also shown better efficiency and better adaptability in complex dynamic environments while it is used for multiagent motion planning applications.
DONS: Dynamic Optimized Neighbor Selection for smart blockchain networks

Blockchain (BC) systems mainly depend on the consistent state of the Distributed Ledger (DL) at different logical and physical places of the network. The majority of network nodes need to be enforced to use one or both of the following approaches to remain consistent: (i) to wait for certain delays (i.e. by requesting a hard puzzle solution as in PoW and PoUW, or to wait for random delays as in PoET, etc.) (ii) to propagate shared data through shortest possible paths within the network. The first approach may cause higher energy consumption and/or lower throughput rates if not optimized, and in many cases these features are conventionally fixed. Therefore, it is preferred to enhance the second approach with some optimization. Previous works for this approach have the following drawbacks: they may violate the identity privacy of miners, only locally optimize the Neighbor Selection method (NS), do not consider the dynamicity of the network, or require the nodes to know the precise size of the network at all times. In this paper, we address these issues by proposing a Dynamic and Optimized NS protocol called DONS, using a novel privacy-aware leader election within the public BC called AnoLE, where the leader anonymously solves the The Minimum Spanning Tree problem (MST) of the network in polynomial time. Consequently, miners are informed about the optimum NS according to the current state of network topology. We analytically evaluate the complexity, the security and the privacy of the proposed protocols against state-of-the-art MST solutions for DLs and well known attacks. Additionally, we experimentally show that the proposed protocols outperform state-of-the-art NS solutions for public BCs. Our evaluation shows that the proposed DONS and AnoLE protocols are secure, private, and they acutely outperform all current NS solutions in terms of block finality and fidelity.
Open-world knowledge graph completion for unseen entities and relations via attentive feature aggregation

Most of knowledge graph completion (KGC) models are designed for static KGs where entity and relation sets are fixed. These approaches are inherently transductive because they simply predict the plausibility of facts whose entities and relations had to previously appear in the training phase. However, since entities and relations are constantly added, removed, or changed over time, KGC models should be able to generalize to out-of-KG entities and relations in evolving KGs, which are more suited to real-world scenarios. Moreover, incorporating global graph-structured information into KGC models is another challenging issue. To overcome these issues, this paper proposes a novel Inductive KG Embedding (IKGE) model for open-world KGC, which accommodates out-of-KG entities and relations. Unlike training individual unique embeddings, the proposed model fundamentally learns an embedding generator function to elaborately generate fact embeddings in an inductive manner. Specifically, the feature information of each fact is extracted as a vector from its entity-related and relation-related side information via an attention mechanism. Then, to score a given fact, our neighborhood feature aggregator hierarchically accumulates the feature information of multi-hop neighbors. Experimental results show that IKGE outperforms existing approaches in both transductive and inductive setups by successfully aggregating neighborhood features.
Differential exponential entropy-based multilevel threshold selection methodology for colour satellite images using equilibrium-cuckoo search optimizer

Recently, the entropic based multilevel threshold selection methods use 2D histogram, which is constructed using the local averages, leading to a loss of edges. Further, the computation of the entropy using the diagonal pixel values only leads to a loss of information. Nevertheless, traditional 2D histogram based multilevel thresholding methods suffer from efficiently retaining the spatial correlation information. In addition, the conventional entropy uses logarithmic function, which has inherent problems, thereby, reducing the accuracy at some situations. To solve these issues, a differential exponential entropy (DEE) -based multilevel threshold selection methodology is proposed. To suppress the high magnitude peaks in the 2D histogram, the normalized local variance is used while the construction. A novel objective function is suggested to compute the DEE. A new Equilibrium-Cuckoo Search Optimizer (ECSO) is suggested to maximize the DEE. For testing, standard benchmark functions are used. The results are compared with the physics-based Equilibrium Optimizer (EO) and the nature-inspired Cuckoo Search Algorithm (CSA). Different benchmark colour satellite images are acquired from the Landsat Image Gallery database for the experiment. The performances are compared with the state-of-the-art methods. Different metrics such as PSNR, SSIM and FSIM are used for the image quality assessment. A statistical analysis is presented in terms of the Box plots. Our proposed DEE-ECSO outperforms the other techniques. The suggested algorithm would be useful for segmentation of the brain MR images for biomedical engineering applications.
BERT-CNN: A Deep Learning Model for Detecting Emotions from Text

Due to the widespread usage of social media in our recent daily lifestyles, sentiment analysis becomes an important field in pattern recognition and Natural Language Processing (NLP). In this field, users’ feedback data on a specific issue are evaluated and analyzed. Detecting emotions within the text is therefore considered one of the important challenges of the current NLP research. Emotions have been widely studied in psychology and behavioral science as they are an integral part of the human nature. Emotions describe a state of mind of distinct behaviors, feelings, thoughts and experiences. The main objective of this paper is to propose a new model named BERT-CNN to detect emotions from text. This model is formed by a combination of the Bidirectional Encoder Representations from Transformer (BERT) and the Convolutional Neural networks (CNN) for textual classification. This model embraces the BERT to train the word semantic representation language model. According to the word context, the semantic vector is dynamically generated and then placed into the CNN to predict the output. Results of a comparative study proved that the BERT-CNN model overcomes the state-of-art baseline performance produced by different models in the literature using the semeval 2019 task3 dataset and ISEAR datasets. The BERT-CNN model achieves an accuracy of 94.7% and an F1-score of 94% for semeval2019 task3 dataset and an accuracy of 75.8% and an F1-score of 76% for ISEAR dataset.