Miracle-dz/UniFAR-FTUs huggingface dataset:
Re-envisioning the Corporation as an Embedded Community

The traditional approach of viewing a corporation as a stand-alone entity operating on its own terms is no longer valid or viable. Changing circumstances force us to re-envision the corporation as an embedded community, operating in harmony with other communities and even eco-systems, and especially with itself. The character of decision-making changes radically in this view, as previous notions of individual discrete decisions, criteria, influence, and authority become broader and more diffuse. Notions of making optimal single decisions must give way to an emphasis on win-win relationships over time. In this paper, we discuss the forces that motivate this re-envisioning of the corporation and articulate an Embedded Community Model that t provides guidance in meeting the challenges required to be successful in this new environment. We discuss how this framework helps to explain the successes as well as the failures experienced in securing support for COVID-19 vaccination. Applications to Technology Planning and Decision Making are also discussed.
A Comparative Analysis for a Novel Hybrid Methodology using Neutrosophic theory with MCDM for Manufacture Selection

The rapid growth of economic makes the process of manufacturing become a political, social, and community concerns. The manufacture selection is a complex multi-criteria decision making (MCDM) issue. The achievement of optimum alternative with respect to manufacture criteria have various diverse procedures. However, recognizing suitable MCDM methods to be adequate for manufacture process is critical for the success to achieve the ideal manufacture. The selection of ideal manufacture decisions is taken in conditions of uncertainty that difficult handled by the traditional methods. Therefore, a proposed hybrid methodology of neutrosophic theory with several MCDM techniques of Analytic Hierarchy Process (AHP), Multi-Objective Optimization based on Ratio Analysis (MULTIMOORA), Multi-Attributive Border Approximation Area Comparison (MABAC), and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to the purposes of manufacture selection. The assessment factors of computational complexity, adequacy to changes of criteria, and agility are applied on the proposed MCDM methods to evaluate sufficiency of the decision process. An empirical study is presented to illustrate the suitability and applicability for suggested methodology. The outcomes demonstrated that proposed hybrid methodology is convenient for manufacture selection. A comparative study is applied, and the results showed, the agility in decision process, AHP technique executed well than MABAC, MULTIMOORA and TOPSIS. The computational complexity, the MABAC technique executed well than AHP, MULTIMOORA and TOPSIS. Additionally, MABAC, MULTIMOORA and TOPSIS techniques are recommended to be the choice of the manufacture selection for adequacy to changes of criteria. Consequently, the comparative study contributes to decision makers and researchers to select the most proper methodology for the process of manufacture selection
Human–Machine Interaction in Intelligent and Connected Vehicles: A Review of Status Quo, Issues, and Opportunities

Human–Machine Interaction (HMI) in Intelligent and Connected Vehicles (ICVs) has drawn great attention in recent years due to its potentially significant positive impacts on the automotive revolution and travel experience. In this paper, we conduct an in-depth review of HMI in ICVs. Firstly, research and application development status are pointed out through the discussion on the cutting-edge technology classification, achievements, and challenges of the HMI technologies in ICVs, including recognition technology, multi-dimensional human vehicle interface, and emerging in-vehicle intelligent units. Then, the human factors issues of ICVs are discussed from three aspects: ICV acceptance, interaction quality of ICVs, and user experience of ICVs. Besides, based on the interaction technology and the mapping of the above issues, we conducted a visual analysis of the literature to realize the reflective thinking of the current HMI in ICVs. Finally, the challenges of HMI technology in ICVs are summarized. Moreover, the promising future opportunities are proposed from three aspects: utility optimization, experience reconfiguration, and value acquisition, to gaining insight into advanced and pleasant HMI in ICVs.
How do Patent Claims Reflect Scientific Knowledge : Insights from Human Genomics

Patent claims are written in legal jargon and primarily read by patent experts. Most scientists and researchers struggle to comprehend scientific knowledge behind patent claims. Drawing upon literature in science-innovation, I ask following questions: 1) how to make patent claims available as early-source information to R&D frontiers? and 2) how to measure the quality of science in each embodiment disclosed within every patent claim? Leveraging on recent extensive curated database of patent citations to scientific articles, I use sentence encoder to reveal the knowledge behind each independent patent claim. I train Bidirectional-Encoder-Representation-Transformer model on patent data in human genomics, then fine-tune with expert validated data, to predict the most similar scientific sentence to each claim based on maximum cosine similarity value. I observe that the first independent claims which reflect quality science are usually shorter than their comparable claims. The extracted scientific sentences can help scientists 1) to detect the presence of simultaneous discovery in order to reduce duplication of R&D expenditures and 2) to locate useful data in large-scale open science resources (e.g. genetic sequences, chemical compounds, etc.).
Towards Semi-Supervised Segmentation of Retinal Fundus Images via Self-Training

Glaucoma is an eye disease that may cause blindness by damaging the optic nerve. The optic cup-to-disc ratio is one of the most important criteria in the diagnosis of glaucoma. However, accurately partitioning a retinal fundus image into optic cup and optic disc regions is crucial to precisely estimating the cup-to-disc ratio automatically. With the emergence of Deep Neural Networks (DNN) and available large-scale manually labeled training data, generic image segmentation has made great progress in recent years. However, large-scale well-labeled medical images are usually expensive and difficult to obtain. To address this problem, in this paper we propose a semi-supervised learning method for retinal fundus image segmentation via self-training based on the MR-Net. The proposed approach uses a self-training semi-supervised learning framework to generate pseudo-labels for unlabeled images. To improve the accuracy of the pseudo-labels, a dense conditional random field is introduced to refine the generated pseudo-labels during the self-training process. Experimental results show that the proposed method remarkably achieves state-of-the-art performance on the RIGA benchmark using only 50% of the annotated data for training, well alleviating the shortage of annotated training data in retinal fundus image segmentation.
Load Cell Force and Direction Sensor System for Push Bars

This paper presents the theory, design, and development of force sensor system using load cells that can provide force magnitude and direction from the handle of a mobile system. A novel mechatronic based patient transfer system is being created as an alternative to sling transfers. This new system is heavy, and staff will require power assistance to move and position the transfer system. Handles on the system provide the expected push bars that staff currently use on gurneys and the proposed sensor system allows the forces on handles to be measured as inputs to a power assist system. The results shown that the sensor provides a linear relationship for force magnitude and allows for the measurement of direction of force with minimal error without requiring detailed calibration of each of the sensors. The results are presented for forces in 8 different directions for a range of force magnitudes. Results show that force magnitude can be estimated with an average standard deviation of 10% and the direction can be measured with an approximately standard deviation of 1%.
A Preliminary Fuzzy Markup Language based Approach for the Queue Buffer Size Optimization in Fog Nodes for Stream Processing

The Internet of Things (IoT) is usually divided in three layers: Edge, Fog, and Cloud layers. The whole IoT infrastructure deals with large amount of data between layers. Focusing on the Fog layer, the sending/receiving data and further cascade processing of those data in the Fog layer enable the Stream Processing paradigm. Thus, aspects such as the number of connections, delays, buffer size, memory usage, among others, have to be considered to optimize the network traffic. Moreover, these characteristics are affected by uncertainty and imprecision since, for example, the number of connections or the buffer size may be considered low in some cases and high in others. Fuzzy Rule-Based Systems (FRBS) are suitable for addressing complex data and managing their imprecision. The objective of this paper is to propose an approach that optimizes network traffic with the main goal of dynamically and automatically adjusting the queue buffer size in a node to avoid network collapse. The IEEE std 1855-2016 for Fuzzy Markup Language and the open source library JFML are used for their flexibility and interoperability offered by these technologies. The proposal has been simulated in three basic different scenarios involving several network traffic states in a fog infrastructure.
A Review of Speech Recognition in Low-resource Languages

There are nearly 7,000 languages in the world, most of which are low-resource minority languages with limited phonetic and linguistic resources. In recent years, with the rapid development of Internet technology and the explosive growth of various audio information, the application of network-based multi-language speech recognition technology is becoming more and more urgent. Therefore, the research demand of speech recognition of small language with low resources is also increasing. This paper briefly introduces the development history and basic principles of speech recognition, summarizes the commonly used speech recognition methods for low resource languages and puts forward the future research direction.
Identifying Areas of Technology Commercialization in the Biomedical Sector: An Integrated Analysis of Patents and Publications

Representative sources of technical information include papers, which are the results of research, and patents, which are the core of technologies. The number of papers and patents is increasing rapidly, and technology commercialization activities in which papers are linked to technology patents are also rapidly developing. Therefore, the necessity of using papers and patents to discover new technological opportunities is emerging. However, despite the semantic similarities between papers and patents, the format and classification systems of documents are very different, so it is difficult to exchange information between documents. This study utilized papers and patent documents to discover new technologies and identify technological opportunities. To overcome the limitations of using paper and patent documents mentioned above, mutual information was utilized by integrating the paper and patent documents and constructing a keyword network using LDA topic modeling and W2Vec methods. In this study, through the results of comparative analysis between papers and patents, first, the research results were uncovered in the white space of technologies that are not linked to patent technologies. Second, a technology potential index is present to evaluate the possibility that the research results can be linked to technology. Third, a technology marketability index that can evaluate the commercialization value of technology is presented. Fourth, through core keyword network analysis, technology trends were identified and technology gaps were discovered, thereby discovering areas in which new technology development is possible. This study contributes to the use of more information by overcoming the limitation of mutual information exchange, even though paper and patent documents contain a lot of technical information. Furthermore, new technologies were discovered, technologies were identified, and technological gaps were uncovered through the results of linking and comparing the paper and patent documents. The technology index, the outcome of this study, enables analyzing the potential and marketability of the technology and evaluating its value. Core keyword network analysis contributed to discovering technical gaps and identifying new technical sectors by utilizing technology connection information through paper and patent documents. This study is expected to be useful for the development of new technologies and the identification of opportunities by presenting information utilization plans through linking papers and patents.
Cough Classification Using Audio Spectrogram Transformer

A variety of technologies can support aging in place, including smart home sensing that can enable independent living through real-time data analysis. In this work, we study cough sound analysis as the cough is a key symptom of many respiratory illnesses and conditions. Based on a data set of cough recordings, we propose a two-pronged approach: the first leverages unsupervised learning to compute intrinsic dimensions of the data and maps raw data for visualizations, and the second uses the insight to train machine learning models through transfer learning on Vision Transformer models. Data augmentation approaches are implemented to improve the performance of the models and our top-performing model achieves an F1-score of 0.804. This study suggests the feasibility of using smart sensing and deep learning for gaining insights into the health of older adults.
ArcFace: Additive Angular Margin Loss for Deep Face Recognition

Recently, a popular line of research in face recognition is adopting margins in the well-established softmax loss function to maximize class separability. In this paper, we first introduce an Additive Angular Margin Loss (ArcFace), which not only has a clear geometric interpretation but also significantly enhances the discriminative power. Since ArcFace is susceptible to the massive label noise, we further propose sub-center ArcFace, in which each class contains <inline-formula><tex-math notation="LaTeX">$K$</tex-math><alternatives><mml:math><mml:mi>K</mml:mi></mml:math><inline-graphic xlink:href="deng-ieq1-3087709.gif"/></alternatives></inline-formula> sub-centers and training samples only need to be close to any of the <inline-formula><tex-math notation="LaTeX">$K$</tex-math><alternatives><mml:math><mml:mi>K</mml:mi></mml:math><inline-graphic xlink:href="deng-ieq2-3087709.gif"/></alternatives></inline-formula> positive sub-centers. Sub-center ArcFace encourages one dominant sub-class that contains the majority of clean faces and non-dominant sub-classes that include hard or noisy faces. Based on this self-propelled isolation, we boost the performance through automatically purifying raw web faces under massive real-world noise. Besides discriminative feature embedding, we also explore the inverse problem, mapping feature vectors to face images. Without training any additional generator or discriminator, the pre-trained ArcFace model can generate identity-preserved face images for both subjects inside and outside the training data only by using the network gradient and Batch Normalization (BN) priors. Extensive experiments demonstrate that ArcFace can enhance the discriminative feature embedding as well as strengthen the generative face synthesis.
Transforming Innovation Management: Implications of Digitalization for the Organization and Management of Innovation in Leading Brazilian Incumbents

The digitalization of businesses is a pervasive tide of innovation whose challenges the literature increasingly addresses. Nevertheless, management research has given much less attention to the implications of digital transformation (DT) for the innovation process and its management. This paper aims to contribute to the debate by exploring the implications of DT for the organization and management of innovation in five leading, innovative Brazilian incumbents. In-depth multiple case studies have been carried out in 2020/21 to produce empirical data on the diffusion of DT and how it has affected innovation strategy, organization, and management practices. Our findings suggest that innovative incumbent firms face a considerable challenge. Competition in their markets continues to require proprietary solutions, technologies, products, and processes as the basis of the current or new business models. So, they must perform both business model innovation and product/service innovation under the diffusion of digitalization. However, the attainment of each of these innovation goals is attributed to distinctive organizational units, which seldom are organically integrated. The companies investigated face the emergence of two poles of innovation, whose orchestration is not trivial.
Keeping Information Alive: Hovering Information and Floating Content Paradigms for Vehicular Networks

Hovering Information and Floating Content are two sub-domains of information dissemination which aim to keep information persistent in a region where vehicle to vehicle (V2V) communication is the only option. In this mutual goal to keep information alive, both sub-domains have sought to design algorithms that overcome the geographical and technological challenges while maintaining efficient resource management. Techniques have arising which stem from flooding and broadcasting techniques, probabilistic communication, and using machine learning to direct communication. This work will examine the state-of-the-art works within both sub-domains to bring the reader to a respectable level of knowledge, and offer insight into the future directions research in the area may take.
Plasmonic Sensors based on 3D-printed polymer waveguides covered by a metals bilayer

The design, development, and testing of a 3D-printed plasmonic sensor has been here presented. A silver and gold bilayer has been deposited on a polymer waveguide to improve the performance of the surface plasmon resonance (SPR) sensor. The developed SPR sensor is very simple to realize, small-size, low-cost, and its sensitivity is sufficient to realize biosensors, exploiting specific receptors in contact with the sensing bilayer. Therefore, the proposed sensing approach could be used to make disposable sensor chips for several application fields, e.g. point-of-care tests and environmental monitoring. Furthermore, numerical and experimental results have demonstrated that the sensor's sensitivity improves when the proposed metals bilayer is used instead of a gold layer.
Discursive Manifestations of Contradictions in Rural Texas Communities

In this study of rural Texas communities, we identify different discursive manifestations of contradictions (DMCs): historically forming disturbances in or long-forming societal tensions that develop in relatively durable activity systems. We contrast C2, which had the highest number of DMCs in our study, to C1, which had the lowest. Based on a detailed analysis of these DMCs, we argue that the high number in C2’s interviews reflects C2’s readiness to recognize each others’ interests and deliberate about a shared future. This analysis suggests that DMCs can help professional communicators to productively analyze discourse to diagnose community concerns—a starting point for constructive community dialogue.
Design of a 10 Bit Low Power Split Capacitor Array SAR ADC

In this paper, a low power split capacitor array structure based successive approximation register (SAR) type analog to digital converter (ADC) is proposed. To minimize power, this ADC combines the capacitive digital to analog converter (DAC) with the sample and hold (S/H) circuit, uses the Split binary-weighted capacitor array for the DAC, and utilizes the open-loop comparator. The ADC consumes low power with good performance. The DAC efficiently uses charge recycling to achieve a high speed of operation. The proposed ADC is designed using 0.18-μm CMOS technology. At a 1.8-V supply and 2 MS/s, the ADC achieves a spurious-free dynamic range (SFDR) of 54 dB and consumes 0.27633 mW.
What are the General Mechanisms that Pushes a Company to Transform Digitally?

There is broad acknowledgment among the majority of leaders in almost every industry that the role of digital technology is rapidly shifting, from being a driver of minor efficiency to a source of significant disruption and innovation. In our fast-paced world of digital and technological advancement, being prepared for digital disruption as well as being able to transform a business digitally is indispensable and will ensure the survival of the organization in the competitive market. Consequently, the growing digitization and digitalization of business processes, products, and services make it imperative for companies to develop a better understanding of the critical drivers for a digital transformation (DT). The digital age already started, and far too many companies do not take advantage of the possibilities or even understand the consequences of the rapidly changing economy. However, in order to stay competitive and to avoid disruption by a new market entrant, incumbents’ companies need to adapt and even remodel their core business models in alignment with the digitalization trend. organizations need to make decisions on which technologies to adopt and how to incorporate them into their day-to-day operations. However, to do this efficiently, organizations first need to understand the critical drivers of DT that affect their business as well as their industry sector. This paper reports the result of an explanatory mixed-method research project to discover the most relevant key drivers for a DT. The quantitative as well qualitative analysis has highlighted two drivers in particular: rising customer expectations and continuously changing customer needs. These two drivers represent the most significant challenge, as well as opportunity companies, face nowadays in the business environment. The overarching objective of this conference paper is to give a universal outlook about the general mechanisms that could alert and subsequently drive a company to undergo a DT.
Self-sustainable IoT Wireless Sensor Node for Predictive Maintenance on Electric Motors

Unexpected equipment failure is expensive and potentially hazardous for workers and users. Periodic inspections and maintenance at predefined intervals aim to limit unplanned production downtime, costly replacement of parts and safety concerns. On the other side, predictive maintenance techniques can monitor equipment as it operates, anticipating deterioration and incoming breakages, enabling just-in-time services at reduced operational costs. This paper presents a deploy and forget predictive maintenance sensor node designed explicitly for industrial electric motors. It is targeted for AC mono and three-phase asynchronous motors and generators, measuring vibrations, environmental noise, temperature, and the external magnetic field. The proposed sensor achieves self-sustainability by exploiting a 4x4 cm thermal source for 72 s with a ∆T of 20 °C, and it features short-long wireless data transfer respectively over WiFi and the cellular NB-IoT network. We tested the prototype on different electric motors, form 4 kW to 110 kW, reporting here its capability to detect anomalies using a vibration spectral analysis.