Miracle-dz/UniFAR-FTUs huggingface dataset:
A Feasibility Study on Metallurgical Slag Classification by Microstructure Recognition

Property of metallurgical slag generated in smelting or refining process of ferrous production can be determined by its microstructure which depends on chemical composition and production process. This study proposes a deep learning method which is a subfield of artificial intelligence for autonomous slag classification by microstructure recognition. This present work focus on the implementation of a convolutional neural network (CNN) to classify four types of slags that the variance microstructure resulted from the difference of their formation condition. Both secondary electron (SE) and backscattered electron (BSE) image type captured by scanning electron microscope (SEM) are used as dataset. ResNet50, InceptionV3 and DenseNet201 network architectures are selected in this study to evaluate their classification performance. In addition, data augmentation manipulated by the software is randomly flipped both horizontally and vertically to avoid overfitting from a limited number of training images. The results showed that the best approach to classification accuracy is reached 98.89% by CNN. Therefore, it can be concluded that CNN is excellent potential method for autonomous slag microstructure classification systems.
A procedure for determining relative densities of brown almond mite populations on almond trees

Brown almond mites reside on woody portions of their fruit tree hosts, from which they make excursions to feed on leaves only during favorable periods of the day. Changes in temperature and possibly in sunlight greatly affect their migrations back and forth on almond trees. Their excursions for food introduce serious errors in sampling whenever attempts are made to compare different populations on a basis of mites counted in samples of plucked leaves. The method described here avoids this difficulty since the mites are counted in samples of leaves and wood. Mites present on a clump of almond foliage are shaken onto a sheet of paper and permanently fixed in position by crushing. Derivation of numerical data from the patterns of crushed mites and appropriate statistical analyses are included in the discussion. The design indicated as being adequate for testing relative efficiency of treatments requires plots of four or five similar trees. Thirty individual twigs from each plot are usually sufficient.
Effect of Welding Parameters on the Tensile Strength of the Weld-Deposition Layer

In general, tensile strength is an important factors when evaluating the quality of a product. In this study, samples are created using Wire Arc Additive Manufacturing (WAAM) technology. WAAM technology is a combination of Metal Active Gas welding technology and Computer numerical control technology, and is used to create products with a near net shape. Experimental process parameters, such as welding speed and welding current, are implemented and a deposition layer test is performed, where samples are deposited, layer-upon-layer, and then face milled to ensure the correct dimensions are achieved before subsequent deposition. Using specialized testing equipment, specific data is obtained in order to determine the tensile strength of the sample. Statistical methods are then used to process the data and establish the effect of the parameters on the tensile strength of the sample.
Predicting delayed instabilities in viscoelastic solids

Describing creep using instantaneous rest lengths explains instabilities in viscoelastic solids and predicts future stability. Determining the stability of a viscoelastic structure is a difficult task. Seemingly stable conformations of viscoelastic structures may gradually creep until their stability is lost, while a discernible creeping in viscoelastic solids does not necessarily lead to instability. In lieu of theoretical predictive tools for viscoelastic instabilities, we are presently limited to numerical simulation to predict future stability. In this work, we describe viscoelastic solids through a temporally evolving instantaneous reference metric with respect to which elastic strains are measured. We show that for incompressible viscoelastic solids, this transparent and intuitive description allows to reduce the question of future stability to static calculations. We demonstrate the predictive power of the approach by elucidating the subtle mechanism of delayed instability in thin elastomeric shells, showing quantitative agreement with experiments.
Spin Alignment Studies on the Muon-Site Determination in La2CuO4

Here we report spin-alignment contributions to muon coordinate calculated utilizing density functional theory (DFT) calculation. We estimated four different antiferromagnetic (AF) spin alignments in La2CuO4. We observed small changes by adjusting spin configurations in DFT calculations. Cu-spin value of 0.61 µB is constant in all calculations. The insulating gap of 1.9 eV is unchanged in all configurations. Muon coordinate was defined as the most minimum energy in atomic potential distribution. By assuming that Cu-spin is a point dipole for each atom, internal fields for muon were calculated and compared to known experimental results.
A Tile-based Interconnect Model for FPGA Architecture Exploration

Modern FPGA has complex interconnect, like curve wires and two-level local muxs (global wires -> block input pins). Existing interconnect model (CB-SB) cannot describe these routing fabrics, hindering CAD exploration of modern FPGA. This paper presents INTB (Interconnect block) model to solve this problem. To represent all routing resources including curve wires and two-level local muxs, a five-side INTB is defined at each FPGA tile. Meanwhile, for simple description of large architecture space, hierarchical model parameters are designed. Besides, we modify CAD flow to apply INTB model: a memory-efficient method based on tile is adopted to generate routing resource graph (RRG); New strategies of routing cost estimation are used to route short and curve wires. INTB model and CAD modifications are implemented in VTR 8.0. The experiments include two parts: first, it is verified that our model is accurate and compatible with CB-SB model. The results show difference of switch area and timing between these two models is 1.21% and 1.11%. Second, our model is used to explore modern FPGA interconnect. Compared to original routing fabrics of VTR, curve wires reduce critical path delay by 11.77% and two-level local muxs reduce channel width by 17.36%.
Is It Approximate Computing or Malicious Computing?

Approximate computing (AC) is an attractive energy efficient technique that can be implemented at almost all the design levels including data, algorithm, and hardware. The basic idea behind AC is to deliberately control the trade-off between computation accuracy and energy efficiency. However, with the introduction of AC, traditional computing frameworks are having many potential security vulnerabilities. In this paper, we analyze these vulnerabilities and the associated attacks as well as corresponding countermeasures. More importantly, we propose the vulnerability at data level and demonstrate that without appropriate security mechanism, adversaries can modify the data and convert a secure and trusted AC process to one that produces unexpected errors in the final output. Furthermore, it is difficult to distinguish whether such errors are caused by the approximation nature of AC or from malicious modification and injection. Finally, we propose the information hiding based countermeasures to defend against both existing attacks and the proposed data level attacks, which helps to answer the question: given an error in AC, whether it comes from approximation or it is maliciously introduced.
Interstitial Lung Disease in Patients With Systemic Sclerosis: Toward Personalized-Medicine-Based Prediction and Drug Screening Models of Systemic Sclerosis-Related Interstitial Lung Disease (SSc-ILD)

Systemic sclerosis (SSc) is an autoimmune connective tissue disease, characterized by immune dysregulation and progressive fibrosis. Interstitial lung disease (ILD) is the most common cause of death among SSc patients and there are currently very limited approved disease-modifying treatment options for systemic sclerosis-related interstitial lung disease (SSc-ILD). The mechanisms underlying pulmonary fibrosis in SSc-ILD are not completely unraveled, and knowledge on fibrotic processes has been acquired mostly from studies in idiopathic pulmonary fibrosis (IPF). The incomplete knowledge of SSc-ILD pathogenesis partly explains the limited options for disease-modifying therapy for SSc-ILD. Fibrosis in IPF appears to be related to aberrant repair following injury, but whether this also holds for SSc-ILD is less evident. Furthermore, immune dysregulation appears to contribute to pro-fibrotic responses in SSc-ILD, perhaps more than in IPF. In addition, SSc-ILD patient heterogeneity complicates the understanding of the underlying mechanisms of disease development, and more importantly, limits correct clinical diagnosis and treatment effectivity. Therefore, there is an unmet need for patient-relevant (in vitro) models to examine patient-specific disease pathogenesis, predict disease progression, screen appropriate treatment regimens and identify new targets for treatment. Technological advances in in vitro patient-relevant disease modeling, including (human induced pluripotent stem cell (hiPSC)-derived) lung epithelial cells, organoids and organ-on-chip technology offer a platform that has the potential to contribute to unravel the underlying mechanisms of SSc-ILD development. Combining these models with state-of-the-art analysis platforms, including (single cell) RNA sequencing and (imaging) mass cytometry, may help to delineate pathogenic mechanisms and define new treatment targets of SSc-ILD.
Formononetin Attenuates Airway Inﬂammation and Oxidative Stress in Murine Allergic Asthma

Allergic asthma has been considered as a respiratory disorder with pathological features of airway inflammation and remodeling, which involves oxidative stress. Formononetin (FMT) is a bioactive isoflavone obtained from Chinese herb Radix Astragali, and has been reported to have notable anti-inflammatory and antioxidant effects in several diseases. The purpose of our study was to elaborate the effects of FMT on asthma and the underlying mechanisms. To establish allergic asthma model, BALB/c mice were given ovalbumin (OVA) sensitization and challenge, treated with FMT (10, 20, 40 mg/kg) or dexamethasone (2 mg/kg). The effects of FMT on lung inflammation and oxidative stress were assessed. In OVA-induced asthmatic mice, FMT treatments significantly ameliorated lung function, alleviated lung inflammation including infiltration of inflammatory cells, the elevated levels of interleukin (IL)-4, IL-5, and IL-13, immunoglobulin (Ig) E, C-C motif chemokine ligand 5 (CCL5, also known as RANTES), CCL11 (also called Eotaxin-1), and IL-17A. In addition, FMT treatments eminently blunted goblet cell hyperplasia and collagen deposition, and remarkably reduced oxidative stress as displayed by decreased reactive oxygen species (ROS), and increased superoxide diamutase (SOD) activity. Furthermore, to clarify the potential mechanisms responsible for the effects, we determined the inflammation and oxidation-related signaling pathway including nuclear factor kappa β (NF-κB), c-Jun N-terminal kinase (JNK), and the transcription factor nuclear factor erythroid 2-related factor 2 (Nrf2). FMT treatments appeared to dramatically inhibit the activation of NF-κB and JNK, significantly elevated the expression of heme oxygenase 1 (HO-1) but failed to activate expression of Nrf2. In conclusion, our study suggested that FMT had the therapeutic effects in attenuating airway inflammation and oxidative stress in asthma.
Handling Missing Sensors in Topology-Aware IoT Applications with Gated Graph Neural Network

Reliable data collection, transmission, and delivery on Internet of Things (IoT) systems is crucial in order to provide high-quality intelligent services. However, sensor data delivery can be interrupted for various reasons, such as sensor malfunction, network failures, and external attacks. Thus, only data from a partial set of sensors may be available. We call it the missing sensor problem. This problem can lead to severe performance degradation at inference time by neural-network-based recognition models trained on the complete sensor set. This paper enhances the robustness of neural network models to the missing sensor problem by introducing a novel feature reconstruction module, named the graph recovery module, that handles missing sensors directly inside the network. Specifically, we consider topology-aware IoT applications, where sensors are placed on a physically interconnected network. We design a novel neural message passing mechanism that logically mimics physical network topology, based on recent advances in graph neural networks (GNNs). We rely on a spatial locality assumption, where only correlations between physically connected sensors are explicitly explored. When encountering missing sensors, information is passed from available sensors to missing sensors to be used to reconstruct their features. Moreover, at each message passing step, we utilize a gating mechanism inspired by Gated Recurrent Units (GRUs) to automatically control information flow between available sensors and missing sensors. We empirically evaluate the reconstruction performance of the graph recovery module with two representative IoT applications; human activity recognition (HAR) and electroencephalogram (EEG)-based motor-imagery classification, on three public datasets. Two different backbone networks are utilized for the tasks. Our design is shown to effectively maintain model performance, suffering only 7% to 18% accuracy loss when as much as 90% of sensors are removed, compared to a drop of 15% to 47% in the accuracy of competing state-of-the-art algorithms under the same conditions. The accuracy gap is largest when more sensors are missing.
Querying Recurrent Convoys over Trajectory Data

Moving objects equipped with location-positioning devices continuously generate a large amount of spatio-temporal trajectory data. An interesting finding over a trajectory stream is a group of objects that are travelling together for a certain period of time. We observe that existing studies on mining co-moving objects do not consider an important correlation between co-moving objects, which is the reoccurrence of the co-moving pattern. In this study, we propose the problem of finding recurrent co-moving patterns from streaming trajectories, enabling us to discover recent co-moving patterns that are repeated within a given time period. Experimental results on real-life trajectory data verify the efficiency and effectiveness of our method.
Show me your living room: investigating the role of representing user environments in AR remote consultations

The study reported here investigates AR based support for remote consultations, in which an on-site user is supported by a remote helper. In such situations, it is important for the remote helper (or, in our case, the consultant) to see the environment of the person asking for support in order to relate to it. Based on literature, we created and tested different mechanisms using a 2D video stream with a captured 2D/3D texturized virtual model of the room. In addition, we compared the often-used way of fixing the remote helper's view to the view of the on-site user with the possibility to move around freely in the 2D/3D model. The aim of the study was evaluating how to support an on-site user wearing an AR HMD. The study tested four conditions composed from these differences and with nine real furniture consultants. In the study, we compared four mechanisms in which the consultants were able to place furniture in the living room of a customer and advise the customer on their purchase. We found that there were hardly any differences in task load, social presence or perceived support between the four different conditions. However, participants had clear preferences for certain conditions and aspects of them. From our analysis, we provide recommendations for the design of mixed reality support for remote consultations.
Blind Image Quality Assessment by Natural Scene Statistics and Perceptual Characteristics

Opinion-unaware blind image quality assessment (OU BIQA) refers to establishing a blind quality prediction model without using the expensive subjective quality scores, which is a highly promising direction in the BIQA research. In this article, we focus on OU BIQA and propose a novel OU BIQA method. Specifically, in our proposed method, we deeply investigate the natural scene statistics (NSS) and the perceptual characteristics of the human brain for visual perception. Accordingly, a set of quality-aware NSS and perceptual characteristics-related features are designed to characterize the image quality effectively. For inferring the image quality, we learn a pristine multivariate Gaussian (MVG) model on a collection of pristine images, which serves as the reference information for quality evaluation. At last, the quality of a new given image is defined by measuring the divergence between its MVG model and the learned pristine MVG model. Thorough experiments performed on seven popular image databases demonstrate that the proposed OU BIQA method delivers superior performance to the state-of-the-art OU BIQA methods. The Matlab source code of the proposed method will be made publicly available at https://github.com/YT2015? tab=;repositories.
Bayesian Networks-based personal data synthesis

Often, confidentiality problems and a lack of original data, make it challenging to analyze user data carefully. In such situations, synthetic data can be used that is more suitable for testing and training marketing strategies, personalized assistants, or behavior analysis systems than the original data. In this paper, the approach for generating synthetic social media profiles data based on Bayesian networks was analyzed. The personal data synthesis problem was considered as the inference of a joint probability distribution from the oriented probabilistic models like Bayesian networks. The quality of this approach in generating VKontakte (VK is the Russian analog of Facebook) social network data was demonstrated and assessed. The Bayesian network approach has shown itself well in the tasks of deriving joint and marginal data distributions, which has led to the production of high-quality synthetic personal data.
COCOA: Content-Oriented Configurable Architecture Based on Highly-Adaptive Data Transmission Networks

In domain of parallel computation, most works focus on optimizing PE organization or memory hierarchy to pursue the maximum efficiency, while the importance of data contents has been overlooked for a long time. Actually for structured data, insights on data contents (i.e. values and locations within a structured form) can greatly benefit the computation performance, as fine-grained data manipulation can be performed. In this paper, we claim that by providing a flexible and adaptive data path, an efficient architecture with capability of fine-grained data manipulation can be built. Specifically, we propose COCOA, a novel content-oriented configurable architecture, which integrates multi-functional data reorganization networks in traditional computing scheme to handle the contents of data during the transmission path, so that they can be processed more efficiently. We evaluate COCOA on various problems: complex matrix algorithm (matrix inversion) and sparse DNN. The results indicates that COCOA is versatile enough to achieve high computation efficiency in both cases.
(Non-)Interacting with conversational agents: perceptions and motivations of using chatbots and voice assistants

Conversational agents (CAs) such as Siri, Alexa, and Google Assistant are increasingly penetrating everyday life. From a Human-Computer Interaction (HCI) perspective, designing CAs that appropriately support the way they are used within daily life is still challenging. While initial design guidelines for human-AI interaction exist, we still know little about how users actually perceive CAs within their daily lives and what aspects motivate their usage of such tools. Within our research, we therefore conducted an interview study with 29 participants to uncover daily positive and negative experiences with CAs. By revealing how users currently perceive CAs, we identify quality criteria that could inform their future design. By evaluating these criteria with respect to existing research discourses about user experience (UX) guidelines for CAs, we contribute to the field by extending these guidelines from an end-user's perspective.
Sustainable Infrastructure Monitoring for Security-Oriented Purposes

As computing and communication infrastructures have gained an ever-increasing role in everybody's life, guaranteeing their reliability has become a critical endeavor. In the face of threats that grow more and more sophisticated, we must turn our attention to the techniques that have the potential to match them and scale with the infrastructure complexity. The current trend in the telecommunication industry towards "softwarized infrastructures" by means of new technologies such as Software Defined Networking and Network Function Virtualization may provide a innovative and effective solutions from this point of view. In this work, we outline a network security monitoring architecture aimed at striking the best trade-off between effectiveness and efficiency. This result is achieved by exploiting the possibility, already enabled by state-of-the-art, yet well tested components for infrastructural orchestration, of dynamic instantiation and composition of functions. We conclude that efficient detection of some classes of network-based denial-of-service attacks is possible, and open the path to mitigation strategies that optimize the usage of resources by deploying and re-configuring them as needed in real-time.
Affinity for technology interaction and fields of study: implications for human-centered design of applications for public administration

Affinity for Technology Interaction (ATI) describes whether a person approaches or avoids interaction with technology [1], making it an important variable in human-centered design. To determine whether ATI will likely remain an important variable in the future, we compared the ATI scores of those who will develop applications (students of media and computer science) with those who will use them in a specific context (students of public administration). We also looked for possible selection biases when conducting online samples regarding technology, by comparing a self-selected online sample of public administration employees with a sample of students of public administration conducted in a classroom, in which no selection biases should have had occurred. To locate the samples in the general population we also compared them with a quota sample of the German population. Results indicate markedly different approaches to technology interaction between the student samples and the online sample being more similar to the media and computer science students. We discuss consequences for the human-centered design process, especially for understanding the users.