Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection of leaf fluorescence from reflectance using hyper-spectrometer

A method for measuring fluorescence from reflectance using hyper-spectrometer was proposed.A LI 1800-12S integrating sphere coupled with an ASD hyper-spectrometer was used to measure leaf apparent reflectance.Different algorithms for fluorescence detection based on long pass filters and short pass filters was presented.The results showed that fluorescence spectra could be derived from reflectance difference spectra under illuminations with and without long pass filter,and that different fluorescence intensity was produced due to different cut-off wavelength.Similarly,fluorescence spectra were derived from reflectance under illumination with short pass filters directly.Obvious differences in fluorescence spectra were founded in different kinds of plant.The results indicate that this technique could be helpful for research on passive vegetation fluorescence sensing and development on chlorophyll fluorescence instruments.
Remote Ultrasonic Imaging of a Wire Arc Additive Manufactured Ti-6AI-4V Component using Laser Induced Phased Array

Additive manufacturing (AM) has been revolutionizing the manufacturing industry due to its ability to significantly reduce waste and produce components with intricate shapes. Laser Ultrasonics (LU) is a non-contact and couplant free method to generate and detect ultrasound. LU can accommodate complex component shapes; thus, it has the potential to provide a reliable in-process inspection method for AM components. In recent years the development of Laser Induced Phased Arrays (LIPAs) helped overcome the inherently low signal amplitudes of LU at the non-destructive, thermoelastic regime. In this paper, the Full Matrix Capture data acquisition method is used and a LIPA of 68 elements is synthesized in post processing. The Total Focusing Method imaging algorithm is applied for ultrasonic imaging. The technique is demonstrated on a highly scattering titanium alloy Wire Arc Additive Manufactured (WAAM) component producing high quality ultrasonic images, accurately imaging defects at depths up to 10mm below the inspection surface.
Spherical Robot Remote Control Development

In this paper, a development of a spherical robot is considered. Actuator of the robot is an omniwheel mobile platform with three independently controlled DC motors. The mobile platform is controlled by wireless gamepad via Bluetooth connection. Controlled variables are angle around vertical axis and linear speed in global coordinate system. Setpoints for each variable are calculated in Python script on Raspberry Pi and are delivered to MCU via serial port. For purpose of observing platform condition TCP connection between Raspberry Pi and desk computer was established via Wi-Fi. Based on data sent by client on Raspberry Pi current state of the mobile platform is visualized in Matlab figure in real time.
A Deep Convolutional Neural Network Classification of Heart Sounds using Fractional Fourier Transform

A computer-aided auscultation system can help in the initial diagnosis of heart diseases. In this work, we propose a binary classification system that uses fractional Fourier transform based Mel-frequency spectral coefficients (FrFT-MFSC) and a 1D deep convolutional neural network. FrFt-MFSC is used to convert the phonocardiogram (PCG) into heat maps using four fractional orders (0.9, 0.95, 1.0, 1.10). We verify the performance of our proposed system using a publicly available data set that was provided by 2016 Physionet/Computing in Cardiology Challenge. Ten-fold cross-validation and holdout test methods are used to evaluate the performance of the system. Classifier performance for various features using different fractional orders is also studied. The 10-fold cross-validation provides a good performance and balanced specificity and sensitivity of 0.97 and 0.95 respectively despite using imbalance data set. The proposed system performance is superior to all the current state-of-the art binary human PCG classification systems.
Sparse Point Cloud Generation Based on Turntable 2D Lidar and Point Cloud Assembly in Augmented Reality Environment

3D surface contour measurement is an important means to obtain the morphological characteristics of objects. By registration the actual 3D surface contour with the 3D model in the computer, the accurate position of the scanned object can be obtained. In view of the shortcomings of other object contour scanning devices based on 2D Lidar, this paper present a turntable whose axis of rotation passes through the scanning center of 2D Lidar. In order to solve the problem of missing the profile information of an object, an object profile measurement system based on turntable 2D Lidar is proposed. Aiming at the problem of insufficient matching precision between the point cloud created by 3D model and the actual scanning point cloud, the method of using the actual scanning point cloud to create the point cloud model library was proposed. Based on this method, the actual assembly simulation is carried out in the augmented reality environment.
Efficient BP-based Decoding Algorithms for QC-LDPC Codes

In this paper we propose to use a self-correction technique with two computationally efficient LDPC decoding algorithms: QRCBP and adjusted min-sum. It is shown that using such a technique allows to improve performance of this algorithms both in error floor and waterfall regions. Also comparison with other BP-based simplified decoding algorithms is presented for three quasi-cyclic LDPC codes.
Gender Obfuscation through Face Morphing

With the advancement of machine learning, facial biometric data has been widely adopted for person recognition. Soft biometrics such as gender, age and ethnicity can be extracted automatically from the facial photographs without permission. This raises privacy concerns since such auxiliary information might be utilized improperly. In this work, we apply face morphing to obfuscate gender information in face images, so that gender classifiers can no longer accurately predict gender, but the resulting images can still be used for identity verification. We further explore the reversibility of our approach and the results show that gender obfuscated through face morphing cannot be recovered or retrieved easily. Our approach is especially useful for an identity verification system which is sensitive to morphing attacks.
Fuzzy Model for Common Vulnerability Scoring System

The Common Vulnerability Scoring System (CVSS) is a widely used open standard for measuring the severity of software vulnerabilities based on defined metric values. It uses optimized equations to combine several aspects of a vulnerability to derive the score. In this paper the possibility of implementing this CVSS calculator in fuzzy logic is discussed, using the Fuzzy Logic toolbox from MATLAB extended in Simulink environment.The model is evaluated by comparing the results of both systems for a few test cases. The possibility of implementing an optimized CVSS calculator using Fuzzy logic is discussed.Taking advantage of the possibility available in Fuzzy logic we discuss the possibility to extend the calculator with domain-specific metrics.
MAD Learn: An Evidence-based Affordance Framework to Assessing Learning Apps

Existing recommendations about how to select or design mobile applications (apps) for learning have been heavily relied on customer and teacher reviews, designer descriptions, and educational theories. There is a lack of evaluation frameworks that are informed by research evidence of how different children interact and use apps. The first version of an evidence-based framework, coined as MAD learn, is presented detailing affordances that hinder or help children's learning, as emerged from relevant studies. To encourage further studies in the field, not only by researchers but also designers and practitioners, a methodological approach to iteratively assess the affordances of mobile apps is also introduced. This is based on (a) visualising the learning design and learning components of a given app and (b) analysing the screen and audio recordings of children's interactions with apps. The proposed approach has been tested with 17 children 5–6 years old who interacted with a maths app. The analysis captured patterns of actual usage, including time spent on different activities, completion rates, communication instances, and number and type of mistakes. Insights revealed that certain design affordances, including instructions, feedback, and help-on-demand, were differently perceived by children, in some cases helping to learn while, in others, hindering it.
Infrared Detection Method for Hypervelocity Impact Based on Thermal Image Fusion

As the number of space debris has increased rapidly in recent years, it poses a major threat to the safety of spacecraft in space, and damage assessment for space debris hypervelocity impact is very important. In order to more comprehensively and accurately describe the damage defects in the infrared inspection data collected by infrared thermal imaging technology, this paper proposes an ultra-high-speed impact damage detection algorithm based on infrared reconstruction image fusion. The algorithm first preprocesses the obtained infrared thermal response image sequence and separates the damage features, and then applies a multi-objective evolutionary optimization algorithm to extract the typical transient thermal response, and then reconstructs the feature infrared images. Finally, image fusion based on guided filtering is performed on the damage reconstruction infrared images. In this paper, several reconstruction images that represent different types of impact damage defect are merged together to improve the detection ability of the algorithm. Infrared detection experiments on damaged specimens obtained from actual hypervelocity impacts verify the effectiveness of the proposed algorithm.
A Survey on Cross-Architectural IoT Malware Threat Hunting

In recent years, the increase in non-Windows malware threats had turned the focus of the cybersecurity community. Research works on hunting Windows PE-based malwares are maturing, whereas the developments on Linux malware threat hunting are relatively scarce. With the advent of the Internet of Things (IoT) era, smart devices that are getting integrated into human life have become a hackers’ highway for their malicious activities. The IoT devices employ various Unix-based architectures that follow ELF (Executable and Linkable Format) as their standard binary file specification. This study aims at providing a comprehensive survey on the latest developments in cross-architectural IoT malware detection and classification approaches. Aided by a modern taxonomy, we discuss the feature representations, feature extraction techniques, and machine learning models employed in the surveyed works. We further provide more insights on the practical challenges involved in cross-architectural IoT malware threat hunting and discuss various avenues to instill potential future research.
Assessment of Porcine Bone Metabolism by Dynamic [18F]Fluoride Ion PET: Correlation with Bone Histomorphometry

UNLABELLED The aim of this study was to quantify regional bone blood flow and [(18)F]fluoride ion influx with [(18)F]fluoride ion PET and correlate the results with specific static and dynamic indices of bone metabolism in healthy pigs. METHODS During continuous ventilation (fractional concentration of oxygen in inspired gas = 0.3), dynamic PET scans 120 min in duration were obtained for 9 mini pigs after intravenous injection of 10.0 +/- 1.2 MBq (mean +/- SD) of [(18)F]fluoride ion per kilogram of body weight. Iliac crest bone biopsies were performed immediately before the PET scan to determine static and dynamic indices of bone metabolism (i.e., the mineral apposition rate) by bone histomorphometry. Kinetic rate constants describing influx (K(1)) and efflux (k(2)) of [(18)F]fluoride as well as chemisorption and incorporation of [(18)F]fluoride (k(3)) and reverse transport (k(4)) were determined for 6 vertebral bodies in each animal. Blood flow estimates (f) were derived from K(1) values corrected for the permeability-surface area product using a previously derived correction algorithm. A rate constant describing the net forward transport rate of fluoride (K(i)) and the fluoride volume flux (K(flux)) derived from a 2-tissue-compartment model was calculated and compared with the results of Patlak graphic analysis (K(pat)). RESULTS A significant correlation was found between mineral apposition rate and K(i) (P < 0.005), K(flux) (P < 0.01), K(pat), K(1), and f (P < 0.05). The values of f, K(i), K(flux), and K(pat) did not correlate significantly with other static or dynamic histomorphometric indices or with age, serum alkaline phosphatase, or parathyroid hormone levels. The values of f and K(i) correlated linearly (y = 0.023 + 0.32x; r(2) = 0.74; P < 0.001). CONCLUSION PET bone studies using [(18)F]fluoride ion provide quantitative estimates of bone blood flow and metabolic activity that correlate with histomorphometric indices of bone formation in the normal bone tissue of the mini pig. Therefore, it seem reasonable to assume that [(18)F]fluoride ion PET can reduce the number of invasive bone biopsies, thus facilitating follow-up of patients with metabolic bone diseases.
Design Reverse Converter for Balanced RNS with Three Low-cost Modules

The development of modern computer technologies makes an urgent task of improving methods of storing, transferring and processing information. One of these methods is the Residue Number System (RNS), based on parallel computing of arithmetic operations [1]–[2]. in This Paper, the Analysis of Three-Module Rns Was Carried Out Using a Measure of Rns Balance, the Crtf Constants for Rns $\{2^{n},2^{n}-1,2^{n-1}-1\}$ Were Calculated, a Device Was Proposed that Implements the Reverse Conversion for Rns $\{2^{n},2^{n}-1,2^{n-1}-1\}$, the Simulation Was Carried Out on Fpga, Which Confirmed the Quality of the Proposed Reverse Conversion. the Simulation Results Have Confirmed that the Proposed Reverse Conversion Takes Fewer Hardware Costs By 10-15%and Higher Speed By 2-25%.
Analysis and initial design of bidirectional acoustic tag modulation schemes and communication protocol

Acoustic underwater tags are key devices to study marine animals and obtain information regarding their behaviour. This information is essential to increase our knowledge of oceanic species and implement efficient conservation policies. At present, all the acoustic tags have a unidirectional communication protocol, which introduces important limitations for their localization such as range measurement, and in situ reconfiguration. To solve these issues and improve the current state-of-the-art of acoustic tags, a new bidirectional tag device has been developed. This new tag will allow new studies and will open a wide tracking capability by using autonomous underwater vehicles and range-based algorithms. The main characteristics of the tag communications scheme such as the chosen modulation, its implementation and the communication protocol are presented on this paper. Also, the simulation software and the conclusions that lead to the initial prototype design are presented.
Patch-Level Operation With Adaptive Patch Control for Improving Anomaly Localization

In realizing unsupervised pixel-precise anomaly localization by utilizing a generative model, a reference image must be generated (for comparison with an input image) by transforming abnormal patterns of an input image, if any, into normal patterns. In this study, a patch-level operation with adaptive patch control is proposed to improve anomaly localization by generating a better reference image. As a way to exploit a generative model, we divide an image into non-overlapped patches of the same size, generate patch-level reference images, and stitch the patch-level reference images into a single reference image. We then conduct anomaly localization by comparing an input image with the stitched, reconstructed image. To effectively apply the patch-level operation, we propose adaptive patch control to determine the number of non-overlapped patches to be applied. For this, we synthesize defective images using normal images and examine how well the candidate methods with different numbers of patches remove the synthesized defects. In the same way, we utilize adaptive patch control to select a promising model among the candidate generative models. Based on experiments conducted using the MVTec Anomaly Detection dataset, we demonstrate that our method outperforms previous existing methods. Under a real-world scenario, our method shows ROC AUC of 0.926, in contrast to the best value of 0.893 from existing studies. Furthermore, we prove the feasibility of the adaptive patch control by showing that the removal of the synthesized defects and the anomaly localization for real defective images are highly correlated.
Empirical Assessment and Characterization of Homophily in Classes of Hate Speeches

In this paper, we investigate homophily in hate speech generation on social media platforms. Homophily plays a signiﬁcant role in information diffusion, sustenance of online guilds, contagion in product adoption, the emergence of topics, and life-cycle on social networks. In the real world, features to utilize in similarity computation are well deﬁned but not on social media platforms. We note that similarity among the users can be deﬁned along with multiple aspects like: proﬁle meta-data, the content generated and style of writing. These derived features are capable of capturing similarity along multiple dimensions, primarily semantic, lexical, syntactical, stylomet-ric and topical. We leverage the important features for author-ship attribution, word embeddings, latent and empath topics to compute lexical, syntactical, stylometric, semantic and topical features. We empirically demonstrate the presence of ho-mophily on a dataset from Twitter along with the different aspects of similarity. Further, we investigate how homophily varies with different hateful types such as hate manifesting in topics of gender , race , ethnicity , politics and nationalism . Our results indicate higher homophily in users associating with topics of racism and nationalism .
CHLORPROMAZINE HYDROCHLORIDE AS A SPECTROPHOTOMETRIC REAGENT FOR OSMIUM(VIII) AND OSMIUM(VI)

Os(VIII) and Os(VI) were detd. by oxidizing chlorpromazine hydrochloride in 2.0-3.5M HCl to form a red-colored product which had an absorption max. at 530 nm. A 12-fold molar excess of the reagent was necessary for full development of the color which was stable for 1 h. The Sandell sensitivities were 0.0152 I¼g Os(VIII)/cm2 and 0.0134 I¼g Os(VI)/cm2. Beer's law was obeyed for 0.08-7.2 ppm Os(VIII) and 0.04-8.6 ppm Os(VI), resp. The effects of acidity, time, order of addn. of reagents, temp. , reagent concn. , and interference from diverse ions were studied. [on SciFinder(R)]
Predicting State Transition in Freezing of Gait via Acceleration Measurements for Controlled Cueing in Parkinson’s Disease

Freezing of gait (FoG) leads to imbalance and falls in Parkinson’s disease. Cues potentially prevent FoG occurrence and unfreeze the patient from FoG. Although FoG detection and its prediction have been intensively studied, its termination is mostly neglected. Continuous cueing after the termination of freezing is annoying and has potential side effects in the long term. In this article, for the first time, we attempt to develop a machine learning approach for the prediction of start and termination of freezing, which can potentially provide automated controlled cueing in Parkinson’s individuals. We hypothesize certain attributes that correspond to the transition from walking to freezing and vice versa. To this end, we propose unique labeling of classes to predict freezing termination as follows: no FoG, pre-FoG (immediate state preceding FoG), FoG, and pre of post FoG (state just before unfreezing). Daphnet dataset, freely available online, was utilized to develop customized attributes measured from accelerometers with the data recorded from ten participants. The high-dimensional attributes were reduced using a principal component (PC) analysis before being fed to the k-nearest neighbor (kNN) classifier for prediction. With 45 PCs, we achieved average (SD) precision, sensitivity, specificity, f1 score, and accuracy of 95.55% (4.60%), 94.97% (4.86%), 99.19% (0.85%), 95.25% (4.72%), and 98.92% (1.56%), respectively, across the four classes while utilizing the attributes from 60 data points ( $\approx 0.93$ s) prior to a given instant. Specifically, for the newly introduced labeling—pre of post FoG, we observed average (SD) precision, sensitivity, specificity, and f1 score of 92.73% (10.15%), 91.50% (10.34%), 99.83% (0.32%), and 92.10% (10.25%), respectively, with 60 previous data points and 45 PCs. These results show the potential of the present approach in the timely deactivation of cues in real time to avoid any side effects of cueing. Enhanced clinical benefit for Parkinson’s patients is the major contribution of this study.
Process based Machine Learning for Energy Optimization in Industrial Enterprises

This paper presents the design, implementation and primary evaluation of a tool that aims to support industrial processes towards the optimization of energy consumption. The authors describe in the first part of this paper the processes followed in an industrial environment and particularly the Product Cost Accounting Information System. The second part is devoted to the technical details of the system that integrates dominant simulation tools for data generation, machine learning algorithms for energy consumption predictions, and all the appropriate mechanisms that deliver the required scalability and flexibility of the system to serve its purpose.
Combined Approach to Anomaly Detection in Wireless Sensor Networks on Example of Water Management System

The paper presents an approach to anomaly detection in wireless sensor networks (WSN). It based on a combination of machine learning and visual data analysis methods. The approach was tested on an example of a water management system presented by a software/hardware prototype and a software model for generating necessary datasets for producing and testing anomaly detection models. The results of the experiments showed high values of the quality indicators for detecting anomalies. Therefore, it indicates the feasibility of application of the combined approach to detecting anomalies for wireless sensor networks in practice.
A Machine Learning Approach for a Vision-Based Van-Herick Measurement System

The application of Artificial Intelligence to the instrumentation and measurements field is nowadays an attractive research area. Indeed, Artificial Intelligence gives the possibility to perform activities also in case of inability to perfectly model a phenomenon or a system. Furthermore, making machines learn from data how to perform an activity, rather than hard code sequential instructions, is a common and effective practice in many modern research areas. This paper investigates the possibility to use Machine Learning techniques in an ophthalmic vision–based system performing automatic Anterior Chamber Angle measurements. Currently, this procedure can be performed only by appropriately trained medical personnel. For this reason, Machine Learning and Vision–Based techniques may greatly improve both test objectiveness and diagnostic accessibility, by allowing to automatically carry out the measurement procedure.