Reviewerly/exHarmony huggingface dataset:
Web User Interface Design of a Visual Editor for Cloud Infrastructure Modeling

Cloud computing has evolved the way IT technicians manage infrastructure resources to support software applications. Leasing equipment and services from cloud infrastructure providers, such as Amazon Web Services, Microsoft Azure, etc., has replaced the traditional strategy of locally installing expensive equipment. Nowadays, IT technicians model the infrastructure they need by writing scripts, then running these scripts in the provider web platform. However, writing scripts becomes a complex task that involves interacting with the Command Line Interface and knowing the commands each provider imposes. In this context, replacing textual with visual interaction becomes a need. The purpose is to fesign the user interface (UI) of a visual web editor to model the cloud infrastructure resources. The User-Centered Design (UCD) methodology was applied to design the UI. As part of DCU, a study of the UIs of diagramming online tools was conducted to identify UI design patterns; those that expert designers use when designing visual editors.The results show that a set of 11 UI patterns for designing visual editor UIs was defined. In addition, a pattern language was built considering the relationships between patterns. By using the pattern language, the visual editor UI design was composed. As conclusions, the pattern language provided a logical way to compose the visual editor UI. In this sense, the proposed UI together with the pattern language can become a reference point for designing UIs in this domain. In future work, the effectiveness of the UI in reducing the complexity of defining the cloud infrastructure will be evaluated with user tests.
Detecting Atypical Behaviors of Taxpayers with Risk of Non-Payment in Tax Administration, A Data Mining Framework

One of the primary processes in tax administration is debt collection management. The objective of this process, among others, is to recover economic resources that have been declared by taxpayers. Due to limitations in tax administration such as staffing, tools, time, and others, tax administrations seek to recover debts in the early stages of control where collection costs are lower than in subsequent stages. To optimize the debt collection management process and contribute to decision-making, this study proposes a deep learning-based framework to detect atypical behaviors of taxpayers with a high probability of non-payment. Normal and atypical behavior groups were also analyzed to identify interesting events using association rules.
The medium is accountable: Metacommunication and media ideologies about voice messages in WhatsApp chats

Voice messages (VMs), which allow users to send recorded messages to other contacts, are a popular feature of instant messaging applications. Despite their popularity, linguistic research on VMs is still in its infancy. This study analyses metacommunication around VMs in mobile messaging conversations among WhatsApp users in Germany and Spain. It focuses on participants’ metacommunicative accounts (such as references, explanations, or motivations) of their preference for audio posting over other forms of communication (e.g. texting). Drawing on recent advances in digital conversation analysis, we examine how accounts placed in different sequential positions in messenger chats (preceding a VM, at the beginning or end of a VM, or after it has been sent) address diverse aspects of voice messaging (from either the sender’s or recipient’s perspective). We demonstrate that accounting accomplishes different social actions, such as framing a VM as something outstanding or worth apologising for. We argue that a sequential analysis of accounting and metacommunication offers rich insights into users’ media ideologies concerning the appropriateness and timing of text and voice messaging. Overall, these findings contribute to a better understanding of the growing importance of voice in mobile communication.
Seismic Sensor Array for Lahar Detection at Cotopaxi Volcano Based on a Datalogger with Field Programmable Gate Arrays

In this work, we have used several configurations of single-component seismic sensors array for the detection and analysis of the signals produced by lahars at the Cotopaxi Volcano, Ecuador. We have developed a compact, flexible, and reliable prototype enough to be used in volcanic monitoring that includes a new generation Field Programmable Gate Arrays (FPGA) as the basis of the embedded system for continuous signal acquisition, digitalization and storage. We have used an array of twelve single-component seismic sensors in linear geometric configuration. The information is also sent to the central base (Instituto Geofísico) located in the city of Quito through radiofrequency communication. Subsequently, the information is analyzed by means of a mathematical model developed during the time of execution of this research as a result, we have obtained a quick lahar detection including calculation of the speed with which the lahar descends, providing reliable information from the moment the event arises, generating an early warning to the affected population.
The Stable Model Semantics of Datalog with Metric Temporal Operators

Abstract We introduce negation under the stable model semantics in DatalogMTL – a temporal extension of Datalog with metric temporal operators. As a result, we obtain a rule language which combines the power of answer set programming with the temporal dimension provided by metric operators. We show that, in this setting, reasoning becomes undecidable over the rational timeline, and decidable in ${{\rm E}{\small\rm XP}{\rm S}{\small\rm PACE}}$ in data complexity over the integer timeline. We also show that, if we restrict our attention to forward-propagating programs, reasoning over the integer timeline becomes ${{\rm PS}{\small\rm PACE}}$ -complete in data complexity, and hence, no harder than over positive programs; however, reasoning over the rational timeline in this fragment remains undecidable.
Behavioral Modeling of Collaborative Problem Solving in Multiplayer Virtual Reality Manufacturing Simulation Games

Abstract Engineering is an inherently creative and collaborative endeavor to solve real-world problems, in which collaborative problem solving (CPS) is considered one of the most critical professional skills. Hands-on practices and assessment methods are essential to promote deeper learning and foster the development of professional skills. However, most existing approaches are based on out-of-process procedures such as surveys, tests, or interviews that measure the effectiveness of learning activity in an aggregated way. It is desirable to quantify CPS dynamics during the learning process. Advancements in virtual reality (VR) provide great opportunities to realize digital learning environments to facilitate a learning-by-doing curriculum. In addition, sensors in VR systems allow us to collect in-process user behavioral data. This paper presents a multiplayer VR manufacturing simulation game for virtual hands-on learning experiences, as well as a behavioral modeling method for monitoring the CPS skills of participants. First, we developed the Virtual Learning Factory, where users play simulation games of various manufacturing paradigms. Second, we collected action logs from a sample of participants and used the same pattern to generate more data. Third, the behavioral data are modeled as dynamic networks for each player. Last, network features are calculated, and a CPS scoring method is driven from them. Experimental results show that the proposed behavioral modeling successfully captures different patterns of CPS dynamics according to manufacturing paradigms and individuals. This detailed assessment contributes to the development of appropriate student-specific interventions to improve learning outcomes.
Pseudo-trilateral adversarial training for domain adaptive traversability prediction

Traversability prediction is a fundamental perception capability for autonomous navigation. Deep neural networks (DNNs) have been widely used to predict traversability during the last decade. The performance of DNNs is significantly boosted by exploiting a large amount of data. However, the diversity of data in different domains imposes significant gaps in the prediction performance. In this work, we make efforts to reduce the gaps by proposing a novel pseudo-trilateral adversarial model that adopts a coarse-to-fine alignment (CALI) to perform unsupervised domain adaptation (UDA). Our aim is to transfer the perception model with high data efficiency, eliminate the prohibitively expensive data labeling, and improve the generalization capability during the adaptation from easy-to-access source domains to various challenging target domains. Existing UDA methods usually adopt a bilateral zero-sum game structure. We prove that our CALI model—a pseudo-trilateral game structure is advantageous over existing bilateral game structures. This proposed work bridges theoretical analyses and algorithm designs, leading to an efficient UDA model with easy and stable training. We further develop a variant of CALI—Informed CALI, which is inspired by the recent success of mixup data augmentation techniques and mixes informative regions based on the results of CALI. This mixture step provides an explicit bridging between the two domains and exposes under-performing classes more during training. We show the superiorities of our proposed models over multiple baselines in several challenging domain adaptation setups. To further validate the effectiveness of our proposed models, we then combine our perception model with a visual planner to build a navigation system and show the high reliability of our model in complex natural environments.
Universal Approach for Calibrating Large‐Scale Electronic and Photonic Crossbar Arrays

Analog electronic and photonic crossbar arrays have been emerging as energy‐efficient hardware implementations to accelerate computationally intensive general matrix–vector and matrix–matrix multiplications in machine learning (ML) algorithms. However, the inevitable nonuniformity in large‐scale electronic and optoelectronic devices and systems prevents scalable deployment. Herein, a calibration approach is reported that enables accurate calculations in crossbar arrays despite hardware imperfections. This approach is experimentally validated in a small‐scale free‐space photonic crossbar array based on cascaded spatial light modulators and demonstrated the scalability and universality of this approach in various large‐scale electronic and photonic crossbar arrays. The improved performance of calibrated crossbar arrays in an ML model inference is further demonstrated to classify handwritten digital images.
Collusion resistant multi-authority access control scheme with privacy protection for personal health records

The personal health record (PHR) is an important part of the smart health system. The medical profession can provide medical services to patients in time by accessing PHRs stored in the cloud. However, PHR suffers from unauthorized access and disclosure of a large amount of private information at present. Privacy protection of personal data and tracking and accountability of malicious users have been important issues in smart health systems. In this paper, we construct an accountable access control scheme based on ciphertext-policy attribute-based encryption (CP-ABE) scheme. Blockchain accounting node distributes the decryption key, and an audit mechanism judges whether the key is leaked by a malicious user or a corrupt node. The authority and user jointly generate the complete decryption key, which reduces the power of authority and resists collusion attacks between revoked users and unrevoked users. The revocation mechanism deletes the malicious users by updating parts of the ciphertext. Furthermore, blockchain records all operations in the form of transactions that can prevent tampering with ciphertext and trace the dishonest behavior of each entity. Security analysis and performance efficiency comparison show that our scheme is more practical and has important practical significance than the existing schemes.
The impact of education level and economic freedom on gender inequality: panel evidence from emerging markets

Gender equality is a critical factor for all ingredients of a healthy society and sustainable development. Therefore, measures to decrease gender inequalities in economic, social, and political life are important for the economic and social development of a society. This study analyzes the influence of education level and economic freedom on gender inequality in emerging markets over the 2000-2020 term through causality and cointegration tests. The results of the causality test uncover a bidirectional causality between education level, economic freedom, and gender inequality. In other words, there exists a mutual interaction among education level, economic freedom, and gender inequality in the short term. Furthermore, the findings of cointegration analysis indicate that education level and economic freedom have a negative impact on gender inequality in the long term, but education level is much more effective on gender inequality than economic freedom in nearly all emerging markets.
Towards a Semantics-Based Recommendation System for Cultural Heritage Collections

While the use of semantic technologies is now commonplace in the cultural heritage sector and several semantically annotated cultural heritage datasets are publicly available, there are few examples of cultural portals that exploit these datasets and technologies to improve the experience of visitors to their online collections. Aiming to address this gap, this paper explores methods for semantics-based recommendations aimed at visitors to cultural portals who want to explore online collections. The proposed methods exploit the rich semantic metadata in a cultural heritage dataset and the capabilities of a graph database system to improve the accuracy of searches through the collection and the quality of the recommendations provided to the user. The methods were developed and tested with the Archive of the Art Textbooks of Elementary and Public Schools in the Japanese Colonial Period. However, they can easily be adapted to any cultural heritage collection dataset modelled in RDF.
Task‐oriented feature hallucination for few‐shot image classification

Abstract Data hallucination generates additional training examples for novel classes to alleviate the data scarcity problem in few‐shot learning (FSL). Existing hallucination‐based FSL methods normally train a general embedding model first by applying information extracted from base classes that have abundant data. In those methods, hallucinators are then built upon the trained embedding model to generate data for novel classes. However, these hallucination methods usually rely on general‐purpose embeddings, limiting their ability to generate task‐oriented samples for novel classes. Recent studies have shown that task‐specific embedding models, which are adapted to novel tasks, can achieve better classification performance. To improve the performance of example hallucination for tasks, a task‐oriented embedding model is used in the proposed method to perform task‐oriented generation. After the initialization, the hallucinator is finetuned by applying a task‐oriented embedding model with the guidance of a teacher–student mechanism. The proposed task‐oriented hallucination method contains two steps. An initial embedding network and an initial hallucinator are trained with a base dataset in the first step. The second step contains a pseudo‐labelling process where the base dataset is pseudo‐labelled using support data of the few‐shot task and a task‐oriented fine‐tuning process where the embedding network and hallucinator are adjusted simultaneously. Both the embedding network and the hallucinator are updated with the support set and the pseudo‐labelled base dataset using knowledge distillation. The experiments are conducted on four popular few‐shot datasets. The results demonstrate that the proposed approach outperforms state‐of‐the‐art methods with 0.8% to 4.08% increases in classification accuracy for 5‐way 5‐shot tasks. It also achieves comparable accuracy to state‐of‐the‐art methods for 5‐way 1‐shot tasks.
The Organizational Resources and Knowledge Management Capability: A Systematic Review

Competitive advantage and sustainability of business organizations can only be achieved when an organization identifies, boosts, and directs its resources toward building its capabilities. The literature identified knowledge management (KM) resources and capabilities as the most critical enablers of organizational performance and innovations. This systematic review identified KM resources (enablers) and their effects on KM capabilities. The review also went into the theories used in the empirical research on the problem. To achieve those goals, the authors summarized 27 quantitative, peer-reviewed studies found in well-known databases and published in the last 5 years between January 2014 and April 2019. The review revealed that the primary organizational KM resources are culture, people, leadership, organization structure, resources, capabilities, strategy and technology. To these, the review added some factors, which are social factors, organizational knowledge and organizational characteristics. The study is significant in finding the most common variables or factors in business KM and their implication of them for enhancing knowledge capability and organizational performance.
Basic secure services for standard RISC-V architectures

Trusted Execution Environments (TEEs) have gained prominence as a means to enhance the security of complex system designs. However, TEE implementations such as ARM TrustZone and Intel SGX have been plagued by numerous vulnerabilities, prompting the research community to focus on identifying and resolving existing design flaws. Concurrently, the emergence of the RISC-V processor architecture presents an opportunity for widespread usage and customization. This research paper delves into the potential of the RISC-V architecture to incorporate TEE functionality, emphasizing the need to identify and mitigate possible pitfalls and vulnerabilities during the design phase. By examining current issues present in established TEE frameworks, we implemented and evaluated real-world services utilized by user applications and operating systems that leverage common TEE features on an up-to-date version of the standard RISC-V Instruction Set Architecture (ISA). Our findings reveal that the existing RISC-V ISA can successfully support secure file storage, cryptographic key management, and remote attestation services, employing standard-compliant unmodified RISC-V cores. However, our results also highlight the absence of mechanisms within RISC-V to ensure secure communication between the system and peripherals, thereby lacking means to interact safely with the user in the event of an operating system compromise. In conclusion, we discuss potential solutions to address this remaining challenge and suggest improvements for future research in securing communication channels and user interaction within the RISC-V architecture.
A YOLOW Algorithm of Water-Crossing Object Detection

Accurately identifying and locating water-crossing objects is of utmost importance for environmental protection. However, traditional detection algorithms often exhibit poor anti-interference performance and low detection accuracy in complex environments. To address these issues, this paper proposes a YOLOW algorithm based on a one-stage object detection algorithm for the automatic identification of water objects. The proposed algorithm incorporates two new modules, namely the SPDCS module and the SPPAUG module, to improve the model’s performance. Specifically, the SPDCS module retains all information in the channel dimension, thereby enhancing the model’s detection accuracy and recognition ability for water-crossing objects. The SPPAUG module performs multiscale feature fusion, which further improves the model’s detection accuracy and recognition ability. Moreover, the C2f module is introduced from YOLOv8 to increase the detection speed. Experimental results on a water-floating object dataset demonstrate that the improved YOLOW model outperforms the standard YOLOv5s algorithm, especially in water-crossing object detection. This research has significant implications for environmental monitoring and protection.
Green Fleet: A Prototype Biogas and Hydrogen Refueling Management System for Private Fleet Stations

Biogas and hydrogen (H2) are breaking through as alternative energy sources in road transport, specifically for heavy-duty vehicles. Until a public network of service stations is deployed for such vehicles, the owners of large fleets will need to build and manage their own refueling facilities. Fleet refueling management and remote monitoring at these sites will become key business needs. This article describes the construction of a prototype system capable of solving those needs. During the design and development process of the prototype, the standard industry protocols involved in these installations have been considered, and the latest expertise in information technology systems has been applied. This prototype has been essential to determine the Strengths, Challenges, Opportunities and Risks (SCOR) of such a system, which is the first step of a more ambitious project. A second stage will involve setting up a pilot study and developing a commercial system that can be widely installed to provide a real solution for the industry.
A novel online multi-task learning for COVID-19 multi-output spatio-temporal prediction

In light of the ongoing COVID-19 pandemic, predicting its trend would significantly impact decision-making. However, this is not a straightforward task due to three main difficulties: temporal autocorrelation, spatial dependency, and concept drift caused by virus mutations and lockdown policies. Although machine learning has been extensively used in related work, no previous research has successfully addressed all three challenges simultaneously. To overcome this challenge, we developed a novel online multi-task regression algorithm that incorporates a chain structure to capture spatial dependency, the ADWIN drift detector to adapt to concept drift, and the lag time series feature to capture temporal autocorrelation. We conducted several comparative experiments based on the number of daily confirmed cases in 20 areas in California and affiliated cities. The results from our experiments demonstrate that our proposed model is superior in adapting to concept drift in COVID-19 data and capturing spatial dependencies across various regions. This leads to a significant improvement in prediction accuracy when compared to existing state-of-the-art batch machine learning methods, such as N-Beats, DeepAR, TCN, and LSTM.