Reviewerly/exHarmony huggingface dataset:
A modular visuo-haptic mixed reality (VHMR) aided prototype technique for in-vehicle human-machine interaction (HMI) evaluations

Virtual Prototyping is a robust method to greatly reduce the labour cost and time required of the vehicle prototype. Still, the absence of the haptic sensory channel degrades the immersive experience evaluation, especially in terms of in-vehicle human-machine interaction (HMI). Hence, this study proposed a modular visuo-haptic mixed reality (VHMR) aided prototype technique suitable for in-vehicle HMI evaluations, which positions itself between pure VR technology and physical object assessment. Technology acceptance model (TAM) was also introduced to empirically investigate the perceived usefulness, ease of use, presence, risk, as well as the behavioural intention of the prototype technique separately. Results revealed that the modular visuo-physical object prototype technique has certain advantages in terms of positioning, usability and user acceptance among the VHMR solutions, and could be adopted as a low-fidelity prototype to build common ground in the R&D team and get quick feedback from users for the agile in-vehicle HMI development.
Listening to what the system tells us: Innovative auditing for distributed systems

Introduction In recent years, software ecosystems have become more complex with the proliferation of distributed systems such as blockchains and distributed ledgers. Effective management of these systems requires constant monitoring to identify any potential malfunctions, anomalies, vulnerabilities, or attacks. Traditional log auditing methods can effectively monitor the health of conventional systems. Yet, they run short of handling the higher levels of complexity of distributed systems. This study aims to propose an innovative architecture for system auditing that can effectively manage the complexity of distributed systems using advanced data analytics, natural language processing, and artificial intelligence. Methods To develop this architecture, we considered the unique characteristics of distributed systems and the various signals that may arise within them. We also felt the need for flexibility to capture these signals effectively. The resulting architecture utilizes advanced data analytics, natural language processing, and artificial intelligence to analyze and interpret the various signals emitted by the system. Results We have implemented this architecture in the DELTA (Distributed Elastic Log Text Analyzer) auditing tool and applied it to the Hyperledger Fabric platform, a widely used implementation of private blockchains. Discussion The proposed architecture for system auditing can effectively handle the complexity of distributed systems, and the DELTA tool provides a practical implementation of this approach. Further research could explore this approach's potential applications and effectiveness in other distributed systems.
Classification and Provenance on Geochemical Lithogenes: A Case Study on Rock–Soil–Sediment System in Wanquan Area of Zhangjiakou, North China

Geochemical lithogenes have been successfully applied as an innovative concept in the field of composition classification and source traceability of geological materials recently. This paper introduces the background of the development of geochemical genes and the construction and application of LG01 and LG03 lithogenes. Based on LG01 and LG03, the LG_CR classification and provenance are applied and verified on a weathering profile, ten gully sedimentary profiles and regional stream sediments in the Wanquan area of Zhangjiakou city, Hebei province, China. The geochemical lithology of the weathering profile shows a gradual variation from basic-like in the bottom rock to acidic-like at the upper soils compositionally with heterogeneity. The classification results on 10 sedimentary gully profiles (each with five samples) indicate that soils at the bottom of the gully system are dominated with 11 types of LG_CR materials, while the top materials are made up of 21 types, reflecting the mixing of the upstream soils. The results of stream sediments from a regional geochemical survey with a scale of 1:200,000 in this area illustrate that the classification results of LG_CR on stream sediments are basically consistent with the petrological results derived from regional geological mapping. Therefore, LG_CR can be used not only as an effective tool for classification and traceability of geological materials but also has great potential in lithological mapping in petrological-overburdened areas.
A simple analytical expression of quantum Fisher and Skew information and their dynamics under decoherence channels

In statistical estimation theory, it has been shown previously that the Wigner–Yanase skew information is bounded by the quantum Fisher information associated with the phase parameter. Besides, the quantum Cramér–Rao inequality is expressed in terms of skew information. Since these two fundamental quantities are based on the concept of quantum uncertainty, we derive here their analytical formulas for arbitrary two qubit X-states using the same analytical procedures. A comparison of these two informational quantifiers for two quasi-Werner states composed of two bipartite superposed coherent states is examined. Moreover, we investigated the decoherence effects on such quantities generated by the phase damping, depolarization and amplitude damping channels. We showed that decoherence strongly influences the quantum criteria during the evolution and these quantities exhibit similar dynamic behaviors. This current work is characterized by the fact that these two concepts play the same role and capture similar properties in quantum estimation protocols.
Self-fulfilling Prophecy in Practical and Automated Prediction

Abstract A self-fulfilling prophecy is, roughly, a prediction that brings about its own truth. Although true predictions are hard to fault, self-fulfilling prophecies are often regarded with suspicion. In this article, we vindicate this suspicion by explaining what self-fulfilling prophecies are and what is problematic about them, paying special attention to how their problems are exacerbated through automated prediction. Our descriptive account of self-fulfilling prophecies articulates the four elements that define them. Based on this account, we begin our critique by showing that typical self-fulfilling prophecies arise due to mistakes about the relationship between a prediction and its object. Such mistakes—along with other mistakes in predicting or in the larger practical endeavor—are easily overlooked when the predictions turn out true. Thus we note that self-fulfilling prophecies prompt no error signals; truth shrouds their mistakes from humans and machines alike. Consequently, self-fulfilling prophecies create several obstacles to accountability for the outcomes they produce. We conclude our critique by showing how failures of accountability, and the associated failures to make corrections, explain the connection between self-fulfilling prophecies and feedback loops. By analyzing the complex relationships between accuracy and other evaluatively significant features of predictions, this article sheds light both on the special case of self-fulfilling prophecies and on the ethics of prediction more generally.
Credibility Evaluation of User-generated Content using Novel Multinomial Classification Technique

Awareness about the features of the internet, easy access to data using mobile, and affordable data facilities have caused a lot of traffic on the internet. Digitization came with a lot of opportunities and challenges as well. One of the important advantages of digitization is paperless transactions, and transparency in payment, while data privacy, fake news, and cyber-attacks are the evolving challenges. The extensive use of social media networks and e-commerce websites has caused a lot of user-generated information, misinformation, and disinformation on the Internet. The quality of information depends upon various stages (of information) like generation of information, medium of propagation, and consumption of information. Content being user-generated, information needs a quality assessment before consumption. The loss of information is also necessary to be examined by applying the machine learning approach as the volume of content is extremely huge. This research work focuses on novel multinomial classification (based on multinoulli distribution) techniques to determine the quality of the information in the given content. To evaluate the information content a single algorithm with some processing is not sufficient and various approaches are necessary to evaluate the quality of content. We propose a novel approach to calculate the bias, for which the Machine Learning model will be fitted appropriately to classify the content correctly. As an empirical study, rotten tomatoes’ movie review data set is used to apply the classification techniques. The accuracy of the system is evaluated using the ROC curve, confusion matrix, and MAP.
Pathological voice classification based on multi-domain features and deep hierarchical extreme learning machine

The intelligent data-driven screening of pathological voice signals is a non-invasive and real-time tool for computer-aided diagnosis that has attracted increasing attention from researchers and clinicians. In this paper, the authors propose multi-domain features and the hierarchical extreme learning machine (H-ELM) for the automatic identification of voice disorders. A sufficient number of sensitive features are first extracted from the original voice signal through multi-domain feature extraction (i.e., features of the time domain and the sample entropy based on ensemble empirical mode decomposition and gammatone frequency cepstral coefficients). To eliminate redundancy in high-dimensional features, neighborhood component analysis is then applied to filter out sensitive features from the high-dimensional feature vectors to improve the efficiency of network training and reduce overfitting. The sensitive features thus obtained are then used to train the H-ELM for pathological voice classification. The results of the experiments showed that the sensitivity, specificity, F1 score, and accuracy of the H-ELM were 99.37%, 98.61%, 99.37%, and 98.99%, respectively. Therefore, the proposed method is feasible for the initial classification of pathological voice signals.
The Arrival of Zero Trust

A discussion with Michael Loftus, Andrew Vezina, Rick Doten, and Atefeh Mashatan.
Contour Modeling Arbitrary-Oriented Ship Detection From Very High-Resolution Optical Remote Sensing Imagery

Under the multiscale distribution, due to dramatic aspect ratio variance leading to prominent arbitrary-oriented character of ships in very high-resolution (VHR) optical remote sensing imagery, how to generate accurate oriented bounding box (OBB) becomes a hot research topic for arbitrary-oriented ship detection. Consequently, in this letter, a concise and effective one-stage anchor-free contour modeling detector called CMDet is proposed for accurate arbitrary-oriented ship detection. Different from currently existed methods via carefully decoupling several independently characteristic parameters for OBB modeling and regression, we resolve the OBB modeling by jointly regressing the contour information. Specifically, the contour information is expressed as a series of Fourier transform coefficients, which are generated by setting up the mapping relation of 1-D Fourier contour coefficients and spatial OBB contour. In addition, a new inherent geometry loss is designed to make detector better learn the geometry information in training phase. After that, the proposed CMDet only needs to predict the correct center point of ships and regress the corresponding entire 1-D Fourier contour coefficients to generate accurate OBB for ship detection. Finally, extensive experiments are carried out on two public OBB ship detection datasets (e.g., HRSC2016 and DIOR-ship), and comparison results demonstrate that the proposed CMDet can obtain the competitive result than the state-of-the-art (SOTA) detectors.
Visualization of Decision Trees based on General Line Coordinates to Support Explainable Models

Visualization of Machine Learning (ML) models is an important part of the ML process to enhance the interpretability and prediction accuracy of the ML models. This paper proposes a new method SPC-DT to visualize the Decision Tree (DT) as interpretable models. These methods use a version of General Line Coordinates called Shifted Paired Coordinates (SPC). In SPC, each n-D point is visualized in a set of shifted pairs of 2-D Cartesian coordinates as a directed graph. The new method expands and complements the capabilities of existing methods, to visualize DT models. It shows: (1) relations between attributes, (2) individual cases relative to the DT structure, (3) data flow in the DT, (4) how tight each split is to thresholds in the DT nodes, and (5) the density of cases in parts of the n-D space. This information is important for domain experts for evaluating and improving the DT models, including avoiding overgeneralization and overfitting of models, along with their performance. The benefits of the methods are demonstrated in the case studies, using three standard benchmarks.
The state-of-the-art of the integration platforms as a service research

As the world is digitalizing in the fast pace, there is a need for more reliable and scalable integration management. One popular option for modern integration management has been Integration Platforms as a Service (iPaaS). iPaaS is usually implemented to replace the old integration implementation models such as the point-to-point integrations or enterprise service buss. In this research, we are focusing on the state of the iPaaS themed research in the academic field. We also investigate what kind of different viewpoints or research gaps there are in the iPaaS research and how the term iPaaS has evolved and become more clearer during the years. In the results of our systematic literature review (n=14), we reveal that the majority of the iPaaS research has been focused on technological approach. However, now there are signs that iPaaS researchers are beginning to emphasize business-intensive approach, such as the importance of integration strategy or the selection of the right iPaaS platform to serve the enterprises business needs. This study calls for the further work in the area of integration management research.
Movie Recommender System Using Parameter Tuning of User and Movie Neighbourhood via Co-Clustering

Recommender System is a information filtering tool to filter the relevant information from given information in the present era of big data. Movie Recommender System is machine learning based autonomous tool that filters the movies from big movie database like netflix, amazon etc according to user preferences. The main focus of this paper is Partitional Weighted co-clustering for Movie Recommender System. The primary objective of this research article is to fine tune the parameters of user and movie neighborhoods by setting different values for row clusters number and column clutsers number parameters of co-clustering. Test results obtained from the Movie database show that the proposed method can bring more accurate personalized recommendations for the movie as compared to existing methods of the order of 7.91%.
Use of Virtual Tools in Teaching-Learning Processes: Advancements and Future Direction

Teaching-learning processes have been affected by the changes that the introduction of information and communication technologies are making to the current global dynamics. This study examines the trends and evolution of the application of virtual tools in teaching-learning processes. Using a bibliometric analysis, 104 articles retrieved using a search equation defined under the PRISMA methodology were analysed. The results allowed the identification of the most influential contributions, authors, and journals, as well as the trends of research carried out in the field, identifying the authors García-Peñalvo, Sánchez-Prieto and Olmos-Migueláñez as the main references in terms of productivity and impact, as well as the journal Computers in Human Behaviour as the most important in academic impact; additionally, it is identified that COVID-19 and online education are among the main emerging concepts, and higher education and the TAM are among the most solid in the research field. Similarly, the research carried out can be classified into four categories based on their main topic: the acceptance of technology; the design of instruments; the design of platforms; and relevant content. Among the main conclusions, it is mentioned that these tools provide aspects of flexibility, coverage and accessibility at all educational levels.
Preservice teachers’ dispositions for technology integration: common profiles in different contexts across Europe

Technology integration in education has a great potential for learning, provided it is based on solid teacher knowledge and pedagogical reasoning. Teacher Technological Pedagogical Content Knowledge (TPACK) is deeply connected with non-rational factors such as beliefs and attitudes – dispositions –, together informing behaviours for technology integration. This article reports on research into preservice teachers' TPACK and dispositions for technology integration when entering initial teacher education (ITE) courses that will likely impact their professionalisation. This is part of a wider multiple case study engaging 288 participants across three European ITE institutions. Participants' TPACK and dispositions were investigated through a questionnaire. Findings suggest four patterns of preservice teachers' dispositional configuration (disengaged, idealist, executive and self-sufficient) shared by the different contexts, albeit at different rates. Further research is ongoing to understand how ITE may engage the different profiles and which factors specifically may affect dispositional configurations in preservice teachers' education.
Evolving natural language processing towards a subjectivist inductive paradigm

Gin reflects on the advantages of developing new natural language processing strategies that invite both researchers and their AI “assistants” into a collaborative and transparent process of inquiry.
Exploring Efficient-Tuning Methods in Self-Supervised Speech Models

In this study, we aim to explore efficient tuning methods for speech self-supervised learning. Recent studies show that self-supervised learning (SSL) can learn powerful representations for different speech tasks. However, fine-tuning pre-trained models for each downstream task is parameter-inefficient since SSL models are notoriously large with millions of parameters. Adapters are lightweight modules commonly used in NLP to solve this problem. In downstream tasks, the parameters of SSL models are frozen, and only the adapters are trained. Given the lack of studies generally exploring the effectiveness of adapters for self-supervised speech tasks, we intend to fill this gap by adding various adapter modules in pre-trained speech SSL models. We show that the performance parity can be achieved with over 90% parameter reduction, and discussed the pros and cons of efficient tuning techniques. This is the first comprehensive investigation of various adapter types across speech tasks.
Memory-Aware Attentive Control for Community Question Answering With Knowledge-Based Dual Refinement

The question answering system in open domain enables a machine to automatically select and generate the answer for questions posed by humans in a natural language form on the website. Previous approaches seek effective ways of extracting the semantic features between question and answer, but the contextual information effects in semantic matching are still limited by short-term memory. As an alternative, we propose an internal knowledge-based end-to-end model, enhanced by an attentive memory network for both answer selection and answer generation tasks by considering the full advantages of the semantics and multifacts (i.e., timescales, topics, and context). In detail, we design a long-term memory to learn the top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> fine-grained similarity representations, where two memory-aware mechanisms aggregate the series of semantic word-level and sentence-level similarities to support the coarse contextual information. Furthermore, we propose a novel memory refinement mechanism with the two-dimensional of writing heads that offer an efficient approach to multiview selection of the salient word pairs. In the training stage, we adopt the transformer-based transfer learning skill to effectively pretrain the model. Experimentally, we compare the state-of-the-art approaches on four public datasets, the experimental results show that the proposed model achieves competitive performance.
Capability Assessment Methodology and Comparative Analysis of Cybersecurity Training Platforms

Cybersecurity training is a key endeavour for ensuring that the IT workforce possess the knowledge and practical skills required to counter the ever-increasing cybersecurity threats that our society is faced with. While some related systems, such as Capture The Flag platforms, have been available for almost one decade, platforms that support full-fledged cybersecurity training exercises have only been released as open source in recent years. Given the complexity of such cybersecurity training platforms, the question that arises is how to meaningfully evaluate and compare their capabilities in order to identify the most suitable solution for a given type of organization and/or training activity. In this paper, we introduce a capability assessment methodology for cybersecurity training platforms that focuses on the three key aspects of training: content representation, environment management, and training facilitation. The assessment tool that we developed is used to evaluate two open-source cybersecurity training platforms, CyTrONE and KYPO. We then conduct a comparative analysis of these two platforms based on our first-hand developer experience with them, and discuss the lessons learned from implementing, deploying and using these platforms. The assessment tool and the detailed technical comparative analysis that we conducted are intended as instruments and references for anyone who plans to deploy or develop cybersecurity training platforms.
Teleportation protocols with non-Gaussian operations: Conditional photon subtraction versus cubic phase gate

In our work, we compare three teleportation protocols: the original protocol, the photon subtraction protocol, and the protocol with a cubic phase gate. We evaluate the fidelity of each protocol using the example of teleportation of the squeezed state and the Schrodinger's cat state. We show that, under equal conditions, the teleportation scheme with a cubic phase gate achieves significantly higher fidelity than the other protocols considered.
Solving the Element Detecting Problem in Graphs via Quantum Walk Search Algorithm (QWSA)

Many quantum algorithms rely on quantum walks, which are the quantum equivalent of a conventional Markov chain. This paper will present the review of already existing classical Markov model and exiting quantum walk algorithms such as coined quantum walks and Szegedy quantum walk. Then we show the quantum walk search algorithm (QWSA) formulation to solve the problem of detecting elements in graphs. We implemented the algorithm which will detect the element and as well as marked every detected vertex in any given graph and measurement phase in quantum algorithm done in the computational form. Finally, we implemented it on several specific graphs, i.e. four-dimensional hypercube.