Miracle-dz/UniFAR-FTUs huggingface dataset:
Conceptualizing IIoT Platform Ecosystem Verticalization

In this paper, a research gap at the interface of ecosystem theory, platforms and market segmentation strategies is addressed by conceptualizing the verticalization of Industrial Internet of Things (IIoT) platforms and their surrounding ecosystems. After an introduction to business-to-business (B2B) platform ecosystems and IIoT platforms as manifestation of B2B platforms, the concept of verticalization as an example for a market segmentation strategy in the digital domain is discussed. Based on an existing generic verticalization framework, the concept will be transferred from its original application in enterprise software systems to the IIoT. A hypothesis is derived that postulates a causal relationship between IIoT platform ecosystem verticalization and the platform owner's competitive advantage for commercializing an IIoT platform in the market. The research contributes to literature on platforms in information systems and strategic management research by proposing platform ecosystem verticalization as a novel verticalization property. Applied as business strategy, this might enable a commercially successful implementation of IIoT platforms.
A Deep Metric Learning-Based Anomaly Detection System for Transparent Objects Using Polarized-Image Fusion

While visual inspection systems have been widely used in many industries, their use in the food and optical equipment industries has been limited. Transparent and reflective materials are often used in these applications, but existing anomaly detection (AD) systems have low accuracy in their detection due to low visibility. Here, we developed an AD system using a polarization camera for reflective and transparent target objects. Two new techniques are developed. First is the polarized image fusion (PIF) technique which suppresses glare from reflective surfaces while highlighting transparent foreign objects. In PIF, four captured polarized images are fused to synthesize a high-quality image according to calculated weight coefficients. The second new technique is an ArcObj-based deep metric learning technique to improve AD accuracy. The proposed system was evaluated in experiments on three datasets: cookie samples wrapped in transparent plastic bags; transparent plastic bottles; and transparent lenses. High AD accuracies in terms of the area under the receiver operating characteristic curve (AUC) were achieved: 0.88 AUC for the cookie dataset; 0.87 AUC for the bottle dataset; and 0.98 AUC for the lens dataset. Compared to the state-of-the-art AD algorithm (Patchcore), the proposed method improved AD accuracy by 0.09 AUC.
Multiple-Valued Logic Physically Unclonable Function in Photonic Integrated Circuits

Physically Unclonable Function (PUF) optical circuits are described and implemented within a Photonic Integrated Circuit (PIC) to enhance certain security properties such as device authentication, anti-tamper properties, or for use as a root-of-trust value. Optical processing is advantageous for security applications such as these since they are less susceptible to eavesdropping and side channel monitoring via the difficulty of observing electromagnetic radiation emissions during PIC operation. We employ the use of State of Output Polarization (SOP) variability arising from inherent stresses, strains and manufacturing tolerances present within a fabricated PIC. A customized optical signal processing element is used in our PUF circuit that contains a very narrow "trench" structure with tiny structural variations induced during fabrication that enhances SOP variability. The tiny structural changes in fabricated PICs are fixed resulting in repeatable SOP variation of the optical signals. To avoid a need for PUF calibration, to increase robustness with respect to input power level variation, measurement device resolution and sensitivity; we show that PUF functionality can be conveniently modeled as a discrete MultipleValued Logic (MVL) function. The proposed MVL PUF formulation avoids the need to characterize and measure exact polarization states as well as enabling the use of PUF output measurement devices that have a wide range of resolution and sensitivity specifications. Several different PUF circuits are implemented within a single fabricated PIC and are experimentally evaluated to demonstrate its efficacy.
Intelligent Attributes of Voice Assistants and User’s Love for AI: A SEM-Based Study

Adoption of Voice Assistants (VA) has been a hot topic of research currently. However, the existing studies are based on traditional models like the Technology Acceptance Model that is less suitable for investigating adoption of AI-based technologies like VAs. This is because the conventional models do not consider the anthropomorphic nature of AI-based technologies that has the potential to form emotional bonds between man and machines. Therefore, in this work we propose artificial autonomy as an intelligent AI—based attribute founded on three task primitives of sensing, thinking, and action autonomy. Further, we use the concept of interpersonal love and apply it in the context of human-AI relationship based on Sternberg’s Triangular Theory of love. Data is collected from 607 participants across two Asian countries. Partial Least Squares (PLS) based Structural Equation Modelling (SEM) has been used for the purpose of model building and testing. The results highlight the importance of artificial autonomy on all the three aspects of love, indicating the presence of consummate love. “Love for AI” is different from “brand love” since in the later case some of the love aspects of passion, intimacy, and commitment are missing. Theoretically, this study improves our understanding of artificial autonomy as an intelligent AI-attribute and the perceptions of love in Information Systems research. The findings can help technology companies develop novel strategies for improving the human-likeliness of these VAs.
Intra- and Intertumoral Microglia/Macrophage Infiltration and Their Associated Molecular Signature Is Highly Variable in Canine Oligodendroglioma: A Preliminary Evaluation

Simple Summary Canine oligodendrogliomas are universally fatal primary brain tumors. Glioma-associated microglia/macrophages (GAMs) have been shown to contribute to immunosuppression and tumor progression in human glioblastoma (GBM). While a robust GAM infiltrate has been observed in canine oligodendrogliomas, their corresponding molecular signature has not previously been explored. The results of this study show that GAMs variably infiltrate canine oligodendrogliomas. We observed marked differences in GAM density within individual tumors and across tumors. We further observed elevations in several GAM-derived pro-tumorigenic molecules, suggesting that GAMs likely contribute to canine oligodendroglioma pathogenesis. However, similar to GAM density, the tumor tissue expression of the majority of molecules assayed demonstrated significant variability. This is in contrast to our previous work on canine astrocytomas, which more consistently demonstrated robust increases in several GAM-derived pro-tumorigenic molecules. This study raises the possibility that the immune microenvironment across oligodendrogliomas and astrocytomas has key differences which may be relevant to future therapeutic targeting. Abstract The goal of this study was to define the glioma-associated microglia/macrophage (GAM) response and associated molecular landscape in canine oligodendrogliomas. Here, we quantified the intratumoral GAM density of low- and high-grade oligodendrogliomas compared to that of a normal brain, as well as the intratumoral concentration of several known GAM-derived pro-tumorigenic molecules in high-grade oligodendrogliomas compared to that in a normal brain. Our analysis demonstrated marked intra- and intertumoral heterogeneity of GAM infiltration. Correspondingly, we observed significant variability in the intratumoral concentrations of several GAM-associated molecules, unlike what we previously observed in high-grade astrocytomas. However, high-grade oligodendroglioma tumor homogenates (n = 6) exhibited an increase in the pro-tumorigenic molecules hepatocyte growth factor receptor (HGFR) and vascular endothelial growth factor (VEGF), as we observed in high-grade astrocytomas. Moreover, neoplastic oligodendrocytes displayed robust expression of GAL-3, a chimeric galectin implicated in driving immunosuppression in human glioblastoma. While this work identifies shared putative therapeutic targets across canine glioma subtypes (HGFR, GAL-3), it highlights several key differences in the immune landscape. Therefore, a continued effort to develop a comprehensive understanding of the immune microenvironment within each subtype is necessary to inform therapeutic strategies going forward.
Unravelling the Release Kinetics of Exchangeable Magnesium in Acid Soil of Nilgiris

Magnesium deficiency is a pervasive and recurrent factor that significantly restricts crop production, primarily attributable to the low levels of exchangeable magnesium (ex-Mg) present in acidic soil conditions. This deficiency exerts a pronounced negative influence on the sustainability and progress of agricultural development. Hence the current study aspired at modeling the kinetics of Exchangeable Magnesium release from 3 fertilizer sources i.e., Epsom salt (MgSO4·7H2O), Magnesite (MgCO3) and Dolomite [CaMg(CO3)2] in the acidic soil of the Nilgiris district in Tamil Nadu, India. Four mathematical models were verified—Power function, parabolic diffusion, Simple-Elovich, and first-order to explain cumulative Mg2+ release. Power function was noticed to be an outstanding empirical equation finely fitted to the experimental data. The intensity, as well as the modality of the release pattern, was predicted by the numerical parameters. The power function as well as Parabolic Diffusion portrayed the Mg2+ release kinetics best as verified by the maximum correlation coefficients (r2). The parabolic diffusion model also designated the data as suitable, signifying diffusion-controlled exchange. From the derived dissolution rates, it was conceivable to agree Epsom salt (MgSO4·7H2O) from which the release was faster than the other two magnesium sources. In conclusion, these outcomes provided an insight into the temporal dynamics of magnesium availability in acidic soil, highlighting the importance of understanding its release kinetics for sustainable agriculture development. The findings contribute to the broader knowledge of magnesium management strategies, aiding in the development of targeted interventions to alleviate magnesium deficiency and optimize crop productivity in acidic soil environments.
Optimizing the Incorporated Amount of Chinese Milk Vetch (Astragalus sinicus L.) to Improve Rice Productivity without Increasing CH4 and N2O Emissions

Chinese milk vetch (CMV) is a leguminous green manure that is commonly cultivated in paddy fields and can partially substitute synthetic nitrogen fertilizer. However, the impacts of incorporating CMV on CH4 and N2O emissions are still a subject of controversy. Therefore, we conducted a field experiment over three years to investigate emissions under different substitution ratios: urea only (CF); incorporating a traditional amount of CMV (MV); and with incorporation ratios of 1/3 (MV1/3), 2/3 (MV2/3), and 4/3 (MV4/3) of MV for partial urea substitution. Compared with CF, MV2/3, MV, and MV 4/3 resulted in increased yields. MV and MV4/3 reduced N2O emissions but increased CH4 emissions by 28.61% and 85.60% (2019), 32.38% and 103.19% (2020), and 28.86% and 102.98% (2021), respectively, resulting in an overall increase in total global warming potential (except for MV in 2021). MV2/3 exhibited a low greenhouse gas intensity value ranging from 0.46 to 0.47. Partial least-squares-path model results showed that CH4 and N2O emissions were influenced by substitution ratios, which indirectly regulated the gene abundances of mcrA and nosZ. Overall, the impact of CMV on CH4 and N2O emissions was determined by substitution ratios. MV2/3, which involved partial substitution of synthetic N fertilizer with 15.0 t ha−1 of CMV, resulted in improved rice productivity without increasing CH4 and N2O emissions, making it a recommended approach in the study area.
Real-Time Performance Analysis of Processing Systems on ROS 2 Executors

ROS (Robot Operating System) is one of the most popular robotic software development frameworks. Robotic systems in safety-critical domains are usually subject to hard realtime constraints, so timing behaviors must be formally modeled and analyzed to guarantee that real-time constraints are always honored at run-time. Although a series of analysis techniques has been proposed to analyze the timing performance of ROS 2, the state-of-the-art still generates pessimistic results for ROS 2 systems modeled as DAG (Directed Acyclic Graph). This paper focuses on the analysis of such systems, and proposes techniques to analyze the timing performance in a more precise manner. Experiments with both randomly generated workload and a case study are conducted to evaluate and demonstrate our results.
Stair-Climbing Robots: A Review on Mechanism, Sensing, and Performance Evaluation

Indoor service robots have been widely introduced in the fields of cleaning, delivery, education, guidance, and healthcare, etc. in indoor environments. The mobility of an indoor service robot is essential to expanding its applications. However, the mobility of existing indoor service robots is highly limited by surrounding indoor environments. For example, a stair is one of the major obstacles that restrict the reachable areas of indoor service robots. Even though many studies have been performed to develop reliable and fast stair-climbing robots based on legged, tracked, wheel-legged and wheel-linkage mechanisms, a market-dominant stair-climbing robot remains unsolved. This review investigates the efforts of engineers devoted to stair-climbing robots. To this end, the locomotion mechanisms of stair climbing robots are classified and their sensing method are summarized. In this review, we also propose useful criteria for evaluating the stair-climbing ability of an indoor service robot. By virtue of these criteria, the stair-climbing performance of existing robots are qualitatively compared. We hope this review helps develop the reliable and fast stair-climbing robots and the reasonable criteria for their performances.
Multi-gate Mixture-of-Expert Combined with Synthetic Minority Over-sampling Technique for Multimode Imbalanced Fault Diagnosis

Compared with traditional multivariate statistical techniques, deep neural networks have been frequently used for single-mode fault diagnosis and have shown promising results. However, in the real world, a complex industrial process may have several modes and fewer fault samples than normal. Although several multivariate statistical techniques focus on multimode fault diagnosis, those methods usually identify modes and then locally diagnose faults while ignoring relative information across modes and data imbalance problems. In this paper, a deep learning-based method combining Multi-gate Mixture-of-Experts (MMOE) and Synthetic Minority Over-sampling Technique (SMOTE) is proposed to address multimode imbalance fault diagnosis. Specifically, MMOE can identify modes and diagnose faults in each mode simultaneously. Furthermore, it investigates the common information of multiple modes by sharing network parameters. Moreover, SMOTE can address the fault imbalance problem by over-sampling minority fault samples. The experiment results show the performance improvements of multimode imbalance fault diagnosis using MMOE-SMOTE on the Tennessee Eastman (TE) Chemical benchmark process.
Cross-Domain Translation Learning Method Utilizing Autoencoder Pre-Training for Super-Resolution of Radar Sparse Sensor Arrays

This study proposes a convolutional neural network based image translation framework to faithfully realize the super-resolution of radar sensor sparse measurement. We exploited the Autoencoder model able to learn a latent representation of input domain data via the iteration of encoding and decoding processes. Based on the belief that the low resolution data retains an essential measurement to fully represent the sparse information of the high resolution counterpart, we assumed that input (source domain of low-resolution denoted by <inline-formula> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula>) and output (target domain of high-resolution denoted by <inline-formula> <tex-math notation="LaTeX">$Y$ </tex-math></inline-formula>) share a common latent code. On the other hand, from the observation that the recovery quality of <inline-formula> <tex-math notation="LaTeX">$Y$ </tex-math></inline-formula>-to-<inline-formula> <tex-math notation="LaTeX">$Y$ </tex-math></inline-formula> translation is far more favorable than the outcome from <inline-formula> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula>-to-<inline-formula> <tex-math notation="LaTeX">$Y$ </tex-math></inline-formula> translation, we performed the pre-training for finding the latent code (<inline-formula> <tex-math notation="LaTeX">$z_{y}$ </tex-math></inline-formula>) of the <inline-formula> <tex-math notation="LaTeX">$Y$ </tex-math></inline-formula> domain data, and subsequently the acquired <inline-formula> <tex-math notation="LaTeX">$z_{y}$ </tex-math></inline-formula> serving as a label for predicting the latent code (<inline-formula> <tex-math notation="LaTeX">$z_{x}$ </tex-math></inline-formula>) of <inline-formula> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula> domain data. As a result, in our proposed network, encoder and decoder parts were trained with separate minimization strategies relative to respective loss functions. For the sake of verifying the effect of the pre-training scheme, we introduced hyper-parameters, <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>, to regulate the training rate for the encoder and decoder parts respectively. As metrics for quantitative assessment, detection rate (DR), false alarm rate (FAR) and ROC curve were utilized. Our network set with 0.9 of <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> and 0.1 of <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> showed a higher ROC curve plot across the whole range of DR and FAR than a basic Autoencoder. DRs of the proposed network and comparative model were respectively 92.23% and 91.00% at FAR of 2%.
Converse magneto-electric coefficient of concentric multiferroic composite ring

The converse magnetoelectric (CME) coefficient of an artificial, multiferroic composite cylinder was determined for two interface boundary conditions; specifically epoxy-bonded and shrink-fit. The composite consists of two concentrically bonded rings with the inner and outer rings made from Terfenol-D and lead zirconate titanate, respectively. The diameter of the inner annulus was 25 mm, and the outer ring diameter was 30 mm. Electric fields ranging from 20 kV/m to 80 kV/m with AC components cycling at frequencies from 4 kHz to 50 kHz were applied for actuation of the composite. A magnetic bias field from 0 Oe to 2300 Oe was applied for enhancement of the CME coefficient. It has been found that the maximum CME for epoxy-bonded rings occurs at lower bias magnetic field than shrink-fitted rings. Resonance for the epoxy-bonded composite was found to be 36 kHz whereas the resonant frequency for the shrink-fit structure was 34 kHz. The maximum CME coefficients were approximately 535 mG/V at 100 Vpp and 330 mG/.
Precise Response Time Analysis for Multiple DAG Tasks with Intra-task Priority Assignment

In many real-time application domains, there are execution dependencies, such tasks may be formulated as multiple Directed Acyclic Graphs (DAGs) and scheduled with intra-task (i.e., intra-DAG) priority assignment. The worst-case completion time of a DAG must be bounded and schedulability analysis must be conducted during the design phase to estimate the required hardware resources. Typical examples include automotive systems and Ultra-Reliable Low Latency Communications (URLLC), which is the “to-business” protocol in 5G technologies, deployed in industrial automation for instance. To bound the execution time of multiple DAGs, there are two key factors to analyze: the intra-task interference for a single DAG and the inter-task interference between DAGs. While extensive efforts have been invested, the existing methods either still contain a large degree of pessimism or are even erroneous due to errors in the derived analysis. In this paper, we first provide an indepth analysis of the limitation and defects of the existing methods. Inspired by these observations, we construct novel response time analysis for multiple DAG tasks with arbitrary intra-task priority assignment. Our analysis precisely accounts for both the intra- and inter-task interference by fully exploring the node parallelism in each DAG as well as between DAGs. Extensive experimental results show that the proposed analysis obtains tighter bounds and improves the system scheduability by at least 300 % compared to state-of-the-art approaches. This improvement is even larger when the scheduling pressure is relatively high, up to 100 % versus 0 % in many cases. This work notably advances the use of response time analysis in industry. Practitioners have to resort to either potentially unsafe measurement results or significant resource over-provisioning when precise analysis is unavailable.
Scheduling Periodic Segmented Self-Suspending Tasks without Timing Anomalies

Timing guarantee is an important aspect and must be ensured for every individual task in real-time systems. Even for periodic tasks, providing timing guarantees for segmented self-suspending tasks is challenging due to timing anomalies, i.e., the reduction of execution or suspension time of some jobs enlarges the response time of another job. The existing worstcase response time analyses for sporadic self-suspending tasks are only over-approximations and lead to overly pessimistic results. In this paper, we focus on eliminating timing anomalies without negative impacts on the worst-case response time (WCRT) analysis when scheduling periodic tasks with segmented selfsuspension behavior. We propose two treatments, segment release time enforcement and segmentpriority modification, and prove that both treatments eliminate timing anomalies. In our evaluation, the proposed treatments achieve higher acceptance ratios in terms of schedulability compared to state-of-the-art scheduling algorithms. We also implement the segment-level fixed-priority scheduling mechanism on RTEMS, and showcase the validity of the treatment segment priority modification.
Reverse Engineering Workload Measure based on Function Classification

Reverse engineering(RE) is the most basic task of network security companies, but how to quantify the workload of the RE is lack of research.Many related researches focus on developing extensive array of tools to support RE or using RE to discover new vulnerabilities.Therefore, we focus on developing a tool to help analysts or the comanpanies to measure their RE workload as well as understanding the binaries in a statistical way.We classify the functions in binary files into 7 types,apply the function classification in our dataset and obtain some findings which can help analysts master the workload of binary files to be reversed in a statistical way.