Reviewerly/exHarmony huggingface dataset:
Explainable Model Fusion for Customer Journey Mapping

Due to advances in computing power and internet technology, various industrial sectors are adopting IT infrastructure and artificial intelligence (AI) technologies. Recently, data-driven predictions have attracted interest in high-stakes decision-making. Despite this, advanced AI methods are less often used for such tasks. This is because AI technology is a black box for the social systems it is meant to support; trustworthiness and fairness have not yet been established. Meanwhile in the field of marketing, strategic decision-making is a high-stakes problem that has a significant impact on business trends. For global marketing, with its diverse cultures and market environments, future decision-making is likely to focus on building consensus on the formulation of the problem itself rather than on solutions for achieving the goal. There are two important and conflicting facts: the fact that the core of domestic strategic decision-making comes down to the formulation of the problem itself, and the fact that it is difficult to realize AI technology that can achieve problem formulation. How can we resolve this difficulty with current technology? This is the main challenge for the realization of high-level human-AI systems in the marketing field. Thus, we propose customer journey mapping (CJM) automation through model-level data fusion, a process for the practical problem formulation known as explainable alignment. Using domain-specific requirements and observations as inputs, the system automatically outputs a CJM. Explainable alignment corresponds with both human and AI perspectives and in formulating the problem, thereby improving strategic decision-making in marketing. Following preprocessing to make latent variables and their dynamics transparent with latent Dirichlet allocation and a variational autoencoder, a post-hoc explanation is implemented in which a hidden Markov model and learning from an interpretation transition are combined with a long short-term memory architecture that learns sequential data between touchpoints for extracting attitude rules for CJM. Finally, we realize the application of human-AI systems to strategic decision-making in marketing with actual logs in over-the-top media services, in which the dynamic behavior of customers for CJM can be automatically extracted.
Secure Internet of Things network using <scp>light‐weighted</scp> trust and <scp>blockchain‐powered PoW</scp> framework

SUMMARY Blockchain technology comprises security algorithms that work efficiently on battery powered devices. Low energy and delay indicate the effectiveness of any security mechanism. However, it is challenging for the algorithms to achieve low energy and delay because of various attacks. To identify and protect against various kinds of IoT attacks, a light‐weighted trust, and blockchain‐powered PoW framework are proposed in this article. A proof of work (PoW) consensus is utilized on the router side to validate the block before the blockchain accepts it. The flexibility of block design is the main benefit of PoW powered blockchain, so it can modify based on IoT applications. Moreover, trust is one of the major issues in IoT networks. Trust can be established among users during node processing and communication between nodes. This trust is significant for transferring the data from source to destination and the uncertainty of service availability in IoT applications. A trust evaluation between participating nodes and the blockchain mechanism are integrated into this work to cover authorization and authentication. The efficiency of the proposed trust model is evaluated in terms of trust evolution and trustworthy network and compared with the existing trust models. In the experimental analysis, the efficiency of the proposed blockchain‐powered network is analyzed in terms of energy, delay and processing time. The experimental outcomes proved that the proposed lightweight trust and blockchain‐powered PoW are more efficient than other approaches.
Deterministic annealing with Potts neurons for multi-robot routing

Abstract A deterministic annealing (DA) method is presented for solving the multi-robot routing problem with min–max objective. This is an NP-hard problem belonging to the multi-robot task allocation set of problems where robots are assigned to a group of sequentially ordered tasks such that the cost of the slowest robot is minimized. The problem is first formulated in a matrix form where the optimal solution of the problem is the minimum-cost permutation matrix without any loops. The solution matrix is then found using the DA method is based on mean field theory applied to a Potts spin model which has been proven to yield near-optimal results for NP-hard problems. Our method is bench-marked against simulated annealing and a heuristic search method. The results show that the proposed method is promising for small-medium sized problems in terms of computation time and solution quality compared to the other two methods.
Characterization of the annual regime of surface solar irradiance over Argentine Pampean Region using GL1.2 satellite-based data

Detailed description of the solar resource (spatial distribution and temporal variations) is highly desirable in the context of climate monitoring, agricultural planning and energy technologies; however, one shortcoming is the low density of solarimetric networks in many extended regions. Satellite-based estimations have become a potential key tool, provided their data show adequate temporal frequency, high resolution and acceptable accuracy. GL1.2 Model currently runs at Brazilian National Institute for Space Research (CPTEC/INPE), based on GOES VIS channel imagery. It was tested for characterizing solar radiation regime over Pampa Region in Argentina, which extends over more than 600,000 km2. Comparison with time series of nine stations of the Pampa network in the period 2011–2018 showed close agreement for daily mean irradiance using 10-days as well as monthly values (mean bias error generally |MBE| < 6 W m-2 standard deviation STD ≈ 10-15 W m-2 and resulting root mean square error RMSE ≈ 10–16 W m−2). Completeness of GL time series and high spatial definition (0.04°) allowed further time and space analysis of solar radiation regime. It confirmed that Pampean region is rather homogeneous with annual mean GL increasing from 185 W m−2 on coastal region up to 220 W m−2 on the western and northern limits. In addition, GL data detects sharper variations in a scale of few kilometers, such as transition between ground and water in oceanic coast or large rivers. Regional interannual variability is low: COV ≈ 3–4%. All stations in Pampa network show a well definite annual cycle, closely followed by GL estimates. Fourier analysis for GL monthly series shows a dominant deterministic one year signal (H8 harmonic), accounting for 93–95% of variance for all stations and throughout the Pampa region, in phase with the end of Southern Hemisphere spring. The H8 amplitude varies from 100 W m−2 along inner continental boundary up to 120 in southwestern and coastal bound. It is concluded that GL 1.2 model data can be used to accurately describe time series of daily mean irradiance, for 10-days and monthly scales and 4 km resolution, providing a "Test Reference Year" (TRY). This is precious in order to describe spatial-temporal patterns of regional solar climate. An otherwise "random signal" of 20 W m−2 is associated to transient meteorological phenomena with durations shorter than 10-days, such as cold fronts or convective activity. Their deterministic and/or statistical structure could be improved by the combined analysis of short-time scales Pampa network and GL data (daily, hourly or even minutely).
Specification is Law: Safe Creation and Upgrade of Ethereum Smart Contracts

Smart contracts are the building blocks of the "code is law" paradigm: the smart contract's code indisputably describes how its assets are to be managed - once it is created, its code is typically immutable. Faulty smart contracts present the most significant evidence against the practicality of this paradigm; they are well-documented and resulted in assets worth vast sums of money being compromised. To address this issue, the Ethereum community proposed (i) tools and processes to audit/analyse smart contracts, and (ii) design patterns implementing a mechanism to make contract code mutable. Individually, (i) and (ii) only partially address the challenges raised by the "code is law" paradigm. In this paper, we combine elements from (i) and (ii) to create a systematic framework that moves away from "code is law" and gives rise to a new "specification is law" paradigm. It allows contracts to be created and upgraded but only if they meet a corresponding formal specification. The framework is centered around a trusted deployer: an off-chain service that formally verifies and enforces this notion of conformance. We have prototyped this framework, and investigated its applicability to contracts implementing three widely used Ethereum standards: the ERC20 Token Standard, ERC3156 Flash Loans and ERC1155 Multi Token Standard, with promising results.
The Impact of Challenge Information Security Stress on Information Security Policy Compliance: The Mediating Roles of Emotions

Information security policy (ISP) compliance of employees has a profound impact on organization. In the context of information technology innovation and information systems upgrade, employees' information security behavior is one of the most crucial elements in the information security management of organizations. Based on the two-dimensional model of challenge-hindrance stressor theory and affective events theory, this study explores the mediating effects of emotions on the relationship between challenge information security stress and ISP compliance.A field quasi-experimental method was used in this study. Materials include the Challenge Information Security Stress Scale, Information System Security Policy Compliance Scale, and Emotions Scale, which were used to form the two-stage questionnaire surveys. Data of 217 employees from three Chinese companies in Shanghai and Beijing that had passed certifications for information security management system (GB/t22080-2008/ISO/IEC 27001:2005) were collected. Bootstrapping method for multiple mediation models and the Process 3.0 plug-in of SPSS 20.0 were used for data analysis.The findings indicate that challenge information security stress has a positive effect on ISP compliance. Challenge information security stress has a positive effect on positive emotions and a negative effect on negative emotions. Positive emotions have mediating effect between challenge information security stress and ISP compliance, but negative emotions have no mediating effect.The research results expand the research scope of challenging stress in the two-dimensional model of challenge-hindrance stressor theory in the context of organizational information security. The findings reveal the mediating effect of positive emotions in challenge information security stress and ISP compliance relationship, which provides empirical support for the application of positive psychology in the field of management.
Detecting context-based in-claim numerals in Financial Earnings Conference Calls

Most investors tend to make decisions after analysing financial documents of organizations available online. These documents include financial reports, conversations, brochures, etc. While reading these documents investors need to ensure that they rely only on facts and do not get swayed away by claims which representatives of organizations make. Thus, it is essential to have an automated system for detecting whether numerals present in financial texts are in-claim. In this paper, we discuss a system for evaluating whether numerals present in financial texts are in-claim or out-of-claim. It is trained on the English version of the FinNum-3 corpus using two variants of the FinBERT model and a BERT model augmented with handcrafted features. Our best model, an ensemble of these 3 models, produces a Macro-F1 score of 0.8671 on the validation set and outperforms the existing baselines.
Hierarchical Bayesian text modeling for the unsupervised joint analysis of latent topics and semantic clusters

Topic modeling can be unified synergically with document clustering. In this manuscript, we propose two innovative unsupervised approaches for the combined modeling and interrelated accomplishment of the two tasks. Both approaches rely on respective Bayesian generative models of topics, contents and clusters in textual corpora. Such models treat topics and clusters as linked latent factors in document wording. In particular, under the generative model of the second approach, textual documents are characterized by topic distributions, that are allowed to vary around the topic distributions of their membership clusters. Within the devised models, algorithms are designed to implement Rao-Blackwellized Gibbs sampling together with parameter estimation. These are derived mathematically for carrying out topic modeling with document clustering in a simultaneous and interrelated manner. A comparative empirical evaluation demonstrates the effectiveness of the presented approaches, over different families of state-of-the-art competitors, in clustering real-world benchmark text collections and, also, uncovering their underlying semantics. Besides, a case study is developed as an insightful qualitative analysis of results on real-world text corpora.
Experimental Study of Posture Detection Using Purely Passive Magneto-Inductive Tags

We designed a wireless and wearable posture detection system for the human body, which uses four measuring coils (anchors) placed on the torso and additional purely passive resonant coils (tags) placed on each limb and the head. Due to low-frequency magneto-inductive coupling between all coils, each different sensor topology, as a result of the user&#x2019;s posture, leads to a different and possibly unique set of input impedances at the anchors. Learning the relationship between each posture and its corresponding impedance pattern via supervised classifiers thus enables low-cost and low-complexity posture detection.Previously, this concept was only investigated via simulation, which showed promising results. We now take the next step by presenting the first experimental study of this concept. As such, we propose a simple design for testing purposes and use it to assess the system&#x2019;s real world posture detection capabilities for 14 different everyday postures. Moreover, we examine the system&#x2019;s robustness against additional disturbances which would occur when using cheaper and simpler measurement equipment. Lastly, we investigate the impact of each individual anchor on the full system performance. Overall, the proposed system classifies all test cases correctly using the direct measurements and also proves to be resistant against additional Gaussian disturbances.
Image registration method using representative feature detection and iterative coherent spatial mapping for infrared medical images with flat regions

In the registration of medical images, nonrigid registration targets, images with large displacement caused by different postures of the human body, and frequent variations in image intensity due to physiological phenomena are substantial problems that make medical images less suitable for intensity-based image registration modes. These problems also greatly increase the difficulty and complexity of feature detection and matching for feature-based image registration modes. This research introduces an automatic image registration algorithm for infrared medical images that offers the following benefits: effective detection of feature points in flat regions (cold patterns) that appear due to changes in the human body's thermal patterns, improved mismatch removal through coherent spatial mapping for improved feature point matching, and large-displacement optical flow for optimal transformation. This method was compared with various classical gold standard image registration methods to evaluate its performance. The models were compared for the three key steps of the registration process-feature detection, feature point matching, and image transformation-and the results are presented visually and quantitatively. The results demonstrate that the proposed method outperforms existing methods in all tasks, including in terms of the features detected, uniformity of feature points, matching accuracy, and control point sparsity, and achieves optimal image transformation. The performance of the proposed method with four common image types was also evaluated, and the results verify that the proposed method has a high degree of stability and can effectively register medical images under a variety of conditions.
Real Time Face Recognition Based Attendance System For University Classroom

Over past few years there have been significant improvements in the field of artificial intelligence. In presented paper an automatic attendance marking setup based on the concept of face recognition has been proposed. Presented system not only mark the attendance but make an excel sheet to keep the record safe. This system successfully identifies the faces from different directions as well. First an HD 1080p camera captures the face images and then after noise reduction, histogram-oriented gradient (HOG) technique is used to detect the fascial features. Dlib face recognition API has been used in this system with 97.38 % accuracy of face recognition. System can recognize all the students present in the frame and make the record of all those students whose features matches with the database. Presented system is also capable of recognizing the students' face from multiple directions. This system can also be implemented to formulate a full proof surveillance set up in certain organization based on the concept of face recognition.
Creating Custom Digital Assistants for the Scientific Laboratory using the HelixAI Platform

Voice technology and fully virtual digital assistants are becoming increasingly prevalent in many industries, including the scientific laboratory. This environment can greatly benefit from the use of hands-free digital assistants due to the fact that scientists regularly need access to tools and information while performing bench work. The use of a digital assistant in this environment has the potential to streamline laboratory work and reduce the chances of human error due to contamination and the context switching involved in moving between experiments and information storage media. Because the particular protocols and reagents used by each laboratory are often different, there is a need to create custom digital assistants for individual laboratories. In this technical brief we describe a custom software and web application, referred to as the HelixAI platform, that can be used to create digital assistants for individual scientific laboratories. Digital assistants created with this platform can be accessed through any Alexa-enabled smart speaker device. Here we describe the process by which labs can use this platform to create their own digital assistants, along with a description of the underlying technology. An assistant containing information from the scientific company New England Biolabs (NEB) has been created using this software and will serve as an example throughout this paper.
A Comprehensive Review of Early Detection of Oral Cancer Using Deep Learning

Oral cancer is the widely witnessed and devastating disease formulating in a transient anticipation in their superior grade. There is a sudden progression in the vicinity of oral cancer imaging technologies that play a significant role in assessing and centralizing the novel perception of buccal cavity inspection and functioning. The image processing techniques have enhanced premature diagnosis and treatment phases of the threatening disease. The main objective is to evolve a systematic oral cancer prediction model utilizing the wholesum concept of classification by applying (FVI) Optical images, MRI and CT images. A hybrid classifier is used for classification purposes. The outcome from the classifier is used to label the input image as a benign and malignant cell. The execution of the suggested model can be calculated using measuring performance like accuracy, sensitivity and specificity.
Inclusive Improvisation: Exploring the Line between Listening and Playing Music

The field of Accessible Digital Musical Instruments (ADMIs) is growing rapidly, with instrument designers recognising that adaptations to existing Digital Musical Instruments (DMIs) can foster inclusive music making. ADMIs offer opportunities to engage with a wider range of sounds than acoustic instruments. Furthermore, gestural ADMIs free the music maker from relying on screen, keyboard, and mouse-based interfaces for engaging with these sounds. This brings greater opportunities for exploration, improvisation, empowerment, and flow through music making for people with disability and the communities of practice they are part of. This article argues that developing ADMIs from existing DMIs can speed up the process and allow for more immediate access for those with diverse needs. It presents three case studies of a gestural DMI, originally designed by the first author for his own creative practice, played by people with disability in diverse contexts. The article shows that system-based considerations that enabled an expert percussionist to achieve virtuoso performances with the instrument required minimal hardware and software changes to facilitate greater inclusivity. Understanding the needs of players and customising the system-based movement to sound mappings was of far greater importance in making the instrument accessible.
Engineering Training in The Context of Digital Transformation

Digital transformations in today&#x2019;s world are dramatically changing communication capabilities, demand for digital competencies, forms, methods, and technologies of engineering training. Accordingly, the purpose of this paper was to analyze new model approaches to specialist training, factors influencing the updating of educational program content and formation of professional and additional competences of future engineers. The results presented in this paper are based on the analysis of experts&#x2019; opinions, questionnaires and interviews with engineering students of universities and colleges of Kazakhstan, as well as teachers of universities, colleges and employees of partner enterprises. The questionnaires and interviews included the issues of integration of engineering educational programs with industry, employers&#x2019; involvement in the educational process, sustainability and adaptability of educational organizations and engineering educational programs offered by them, selection of effective tools to achieve educational goals. To process the survey results, the method of analyzed factors ranking was proposed in the paper. This method allowed revealing the imbalance in taking into account the opinions of key stakeholders in the development and implementation of educational programs. This approach can be used to create a graduate profile based on the assessments of various key stakeholders and allows responding flexibly to the needs for certain competences of the modern labor market. The results of the research allowed us to formulate practical recommendations for creating a strategically integrated model of digital space for engineering education.