Miracle-dz/UniFAR-FTUs huggingface dataset:
Active Circuits With Diodes: Topological Conditions Sufficient to Determine the State of a Diode

Let <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> be a circuit composed of a finite number of positive and negative linear resistors, ideal diodes, nullators, norators and independent current and voltage sources. In this article, we consider the problem to determine, without any numerical computation, the state of a diode of <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula>. We propose a set of topological conditions such that, when verified by a diode of <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula>, the state of the diode is determined and the same in all the solutions of <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula>. Our results may simplify, sometimes dramatically, the usual trial-and-error procedure to find the solutions of <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula>.
Distributed MAS with Leaderless Consensus to Job-Shop Scheduler in a Virtual Smart Factory with Modular Conveyors

Increasingly intelligent and adaptable manufacturing systems are required to meet the current demands for customization and more excellent responsiveness in the factories. Multi-agent technology can be used to make these systems compatible with Industry 4.0 concepts, integrating elements of the factory that communicate and collaborate continuously, resulting in a more optimized and self-organized production. This article presents a distributed Multiagent System (MAS) that interacts with elements of a virtual Smart Factory, developing behaviors to control the production and logistics of modular conveyors at the factory. A Job Shop Scheduling algorithm, supported by a leaderless consensus mechanism, allows the execution of tasks with different processing orders. The system can pre-adjust its transport route to pass certain groups of machines in the required order.
Multimodal Emotion Recognition for Human Robot Interaction

Emotion recognition is one of the most important assets that humans possess, which helps them to communicate with each other in the most efficient manner. Humans recognize each other’s emotions through facial features, audio features and body gestures. Limited work is reported in literature, wherein multiple modalities are considered for emotion recognition. In this work, models are trained on facial and speech samples using 1-Dimensional (1D) and 2-Dimensional (2D) Convolutional Neural Networks (CNN) and also trained using pretrained networks such as Visual Geometry Group-16 (VGG-16) and Inception Version 3 (V3). Suitable features are extracted from face and speech to classify the emotions. Analysis of speech features is performed to highlight the difference between the expressiveness based on genders. For the final combined model both facial and speech models are trained separately and combined based on a decision tree, which depends on the individual model’s test case accuracy.
Deep Learning and Late Fusion Technique in Medical X-ray Image

Classification of images is essential in medical database because of the different image modalities such as Xray images, Computed Tomography (CT) images, Magnetic Resonance imaging (MRI) etc. In addition to the varieties of image modalities present in different databases, they are of different body parts and need to be properly classified to enhance effective retrieval for purposes such as medical diagnosis, teaching and research. Most of the hand-crafted techniques have various limitations which reduce their potentials to accurately classify medical X-ray images. For more than a decade, various researchers have employed the use of different handcrafted techniques for medical image classification. However, the major problem associated with the techniques is their inability to extract discriminative features that are relevant enough to accurately classify medical images such as radiographic images. This study focuses on employing deep learning and fusion technique to classify medical X-ray images. The proposed technique uses a single pre-trained neural network and a late fusion technique for the classification of ImageCLEF 2007 and 2015 dataset. The employment of a single pre-trained neural network, both as a feature extractor and as a fine-tuned network, makes the technique unique especially when considering the nature of the dataset used. The combination of the posterior probabilities generated from SVM and Softmax classifiers using a single deep pre-trained neural network produces an overall classification accuracy of 95.54% in classifying the dataset into 116 categories on ImageCLEF 2007. This is the highest when compared to the use of AlexNet + SVM and fine-tuned AlexNet alone which produced 84.35% and 86.47% classification accuracies respectively. On ImageCLEF 2015, it produces an overall classification accuracy percentage of 87.72%.
Application Specific System Identification for Model-Based Control in Self-Driving Cars

Linear Parameter Varying (LPV) models can be used to describe the vehicular lateral dynamic behavior of self-driving cars. They are particularly suitable for model-based control schemes such as model predictive control (MPC) applied to real-time trajectory tracking control, since they provide a proper trade-off between accuracy in different scenarios and reduced computation cost compared to nonlinear models. The MPC control schemes use the model for a long prediction horizon of the states, therefore prediction errors for a long time horizon should be minimized in order to increase the accuracy of the tracking. For this task, this work presents a system identification procedure for the lateral dynamics of a vehicle that combines a LPV model with a learning algorithm that has been successfully applied to other dynamic systems in the past. Simulation results show the benefits of the identified model in comparison to other well-known vehicular lateral dynamic models.
Studies on postharvest calcium chloride application on storage behaviour and quality of Asian pear during cold storage

Acrylamide is a potential health hazardous compound, occurring in baked and fried food as a result of excessive dry heating during preparation and/ or processing of foods. This study aimed at investigating the acrylamide content in selected fried and baked foodstuffs commonly consumed in Jordan and comparing the effect of different processing factors on its level. Seventeen food items representing four groups of food (breads, fine bakery products, Arabic sweets and fried foodstuffs) were chosen and purchased from the local markets after collecting data on their formulation and preparation or processing conditions. The selected foods are produced under different conditions (ingredients, method of preparing, pH, heating time and temperature). Furthermore, a controlled study was conducted on four types of foods (falafel, fried kobbeh, potato and karabeej Halab). Acrylamide content was extracted with 2–butanone and analyzed via GC/MS/EI. Acrylamide was found in 15 out of 17 of the tested food samples at varying levels as a result of the differences in formulation and preparation or processing conditions. Acrylamide content of yeast fermented Arabic bread that is usually baked at high temperature and short time (35-45 sec. at 450-500oC for thin Lebanese type and 60-90 sec. at 300-350oC for thick kmaaj type), boiled potatoes and unheated dough of falafel, fried kobbeh and karabeej Halab were below the detectable level (180 µg kg -1 ) of acrylamide. Whereas the Arabic bread leavened by yeast and sodium bicarbonate (mashrooh) or solely by sodium bicarbonate (shrak) and baked under comparable conditions of Arabic bread were found to contain acrylamide at levels of 1200 and 1800µg kg -1 respectively. This demonstrates the effect of increasing pH, due to the addition of sodium bicarbonate as a leavening agent. In comparison to the rapidly baked Arabic bread, hamam bread that is a loaf type, baked at 280oC for 18 min. and having a well developed brown crust showed an acrylamide average of 3300µg kg -1 . Fine (cracker type) bakery products, Arabic sweets and fried foods, their pH values ranging between 6.2 to 8 showed relatively high concentrations of acrylamide (2400-5200 µg kg -1 ). Kirshaleh baladieh that is leavened by both bacterial flora and sodium bicarbonate was found to have the highest value (5200µg kg -1 ) among cracker types which contain 4300, 4700 and 4700 µg kg -1
A Reconfigurable Binomial Weighted Phased Array Antenna Design for Wi-Fi Band

In this study, a reconfigurable binomial weighed phased array design is proposed for the unlicensed Wi-Fi band. The analytical calculations, numerical simulations and the experimental analysis are conducted. The antenna array operates at 2.45 GHz. By analog beamforming and the binominal weighting, the antenna has three distinct main lobes for each corresponding phased configuration with reduced sidelobes. These distinct main lobes have three different directions. The simulations are done on High Frequency Structure Simulator (HFSS) and experimental measurements are carried out on the fabricated prototype. The numerical and the experimental results are presented. Produced antenna has the scattering parameter |S11| less than −15 dB and has the capacity of beamforming in the elevation plane ±30 with two different phase configurations. The gain of the antenna is found as 9 dBi in the simulation for the operating frequency.
Pathogens and Heavy Metals Concentration in Green Leafy Vegetables

Presence of heavy metal and bacterial pathogen in randomly collected samples of green leafy from various stations of Bengaluru city was detected. Heavy metals (cadmium, zinc, copper, iron, chromium, nickel and lead) were analyzed by tri-acid digestion method. The presence of heavy metals in general was in the order of Cd>Zn>Cu>Fe>Cr>Pb. Trace metal concentration in all green leafy vegetables of stations 1-5 were within permissible limit and it has been exceeded in station 6-10. This indicated high levels of soil contamination pose potential danger for the vegetables grown in the vicinity of Arakere lake, Bannerghatta road, Gottigere lake, Naganaikanakere, Bommasandra lake, Hulimavu lake, Kelaginakere and Amblipura lake. The total bacteria and coliforms were enumerated on TSA (Tryptone Soya Agar) and VRBA (Violet Red Bile Agar) media respectively. The total bacterial count in randomly collected samples of coriander ranged from 296 cfu/g to 8 cfu/g, in palak from 16 cfu/g to 0.9 cfu/g, whereas in case of cabbage was 104 cfu/g to 0.9 cfu/g which is an indication of improper pre-harvest and post harvest handling.
Introspective Black Box Failure Prediction for Autonomous Driving

Failures in autonomous driving caused by complex traffic situations or model inaccuracies remain inevitable in the near future. While much research is focused on how to prevent such failures, comparatively little research has been done on predicting them. An early failure prediction would allow for more time to take actions to resolve challenging situations. In this work, we propose an introspective approach to predict future disengagements of the car by learning from previous disengagement sequences. Our method is designed to detect failures as early as possible by using sensor data from up to ten seconds before each disengagement. The car itself is treated as a black box, with only its state data and the number of detected objects being required. Since no model-specific knowledge is needed, our method is applicable to any self-driving system. Currently, no public data of real-life disengagements is available. To test our approach, we therefore use autonomous driving data provided by BMW that was collected with BMW research vehicles over multiple months. We show that an LSTM classifier trained with sequences of state data can predict failures up to seven seconds in advance with an accuracy of more than 80%. This is two seconds earlier than comparable approaches from the literature.
Effect of the Ammonium ChlorideConcentration on the Mineral Medium Composition â€“ Biodegradation of Phenol by a Microbial Consortium

Phenol and its homologues are aromatics containing hydroxyl, methyl, amide and sulphonic groups attached to the benzene ring. These molecules are both anthropogenic and xenobiotics. Phenols are environmental pollutants discharged through wastewaters from various industries. Phenols are toxic to several biochemical reactions. However biological transformation of phenols to non-toxic entities exists in specialized microbes, owing to enzymatic potential involving enzymes of aromatic catabolic pathways. In this study, a series of experiments were performed to examine the effects of the mineral medium composition and the pH on phenol removal. In this purpose, phenol biodegradation was carried out in a batch reactor containing mixed bacteria; the temperature (30°C), the stirring velocity (200 r /min) and the phenol concentration (125 mg/L) were kept constants. The initial pH was varied in the range 5 - 9 and the mineral components were tested in the following concentration ranges: 0 - 2 g/L for NH 4 Cl, 0 - 4 g/L for KH 2 PO 4 , 0 - 4 g/L for NaH 2 PO 4 and 0 - 0.2 g/L for MgSO 4 . Their effects on phenol biodegradation and specific growth rate were examined. All experiments were carried out at a given initial bacterial concentration of 0.08 g/L (based on optical density determination, 0.079). The shorter biodegradation time of phenol was 20.6 h for NaH 2 PO 4 , KH 2 PO 4 and MgSO 4 concentrations of 2 - 4, 3 and 0.2 g/L respectively. Maximum specific growth rate (0.65 h -1 ) and total
Large-Scale Semantic 3-D Reconstruction: Outcome of the 2019 IEEE GRSS Data Fusion Contest—Part B

We present the scientific outcomes of the 2019 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society. The contest included challenges with large-scale datasets for semantic 3-D reconstruction from satellite images and also semantic 3-D point cloud classification from airborne LiDAR. 3-D reconstruction results are discussed separately in Part-A. In this Part-B, we report the results of the two best-performing approaches for 3-D point cloud classification. Both are deep learning methods that improve upon the PointSIFT model with mechanisms to combine multiscale features and task-specific postprocessing to refine model outputs.
Butler Matrix Based Beamforming Networks for Phased Array Antenna Systems: A Comprehensive Review and Future Directions for 5G Applications

Due to the rapid development of wireless communication technologies, the number of wireless users are radically increasing. Currently, $\sim 23$ billion wireless devices are connected to the internet, and these numbers are expected to increase manifolds in the years to come. The technology growth of the fifth-generation (5G) wireless systems will be needed to meet this high demand of the network. 5G wireless systems offer data-rates of up to 10Gbps, 1-ms latency, and reduced power consumption. It is a known fact that 5G wireless systems will be exploiting beyond the presently used 3 GHz microwave and millimetre-wave (mm-wave) frequency bands. This is the primary driver in the development of the 5G wireless system. Multi-beam Phased array antenna (PAA) systems are typically used in the deployment of 5G systems for high-gain and directionality. In current 5G and future Beyond 5G (B5G) antenna array systems, beamforming networks (BFNs) such as the Butler Matrix (BM) will play a key role in achieving multi-beam characteristics. So, this paper presents an extensive review of the BM based BFNs, and discusses which type of BM will be suitable for the phased array antenna (PAA) systems in the upcoming 5G and next-generation of B5G wireless systems. Moreover, this paper also summarizes the different types of BM designs based on the number of layers. The BMs are classified into the bi-layer, tri-layer, and four-layer structures. It includes different techniques that have been used to solve the problem of crossing, narrow bandwidth, and size reduction of the BM. From the previous studies, it is found that most of the past research work was performed using the bi-layer BM system, whereas the difficult geometries like tri- and four-layer BM are avoided due to their complex fabrication process. It is also found in this paper that the metamaterial (MTM) based bi-layer BM achieves low insertion-loss and phase-error, excellent bandwidth and compact size, and good S-parameter performance, which makes them an ideal BFN candidate for the upcoming 5G and next-generation B5G systems.
Clustering Traffic Scenarios Using Mental Models as Little as Possible

Test scenario generation for testing automated and autonomous driving systems requires knowledge about the recurring traffic cases, known as scenario types. The most common approach in industry is to have experts create lists of scenario types. This poses the risk both that certain types are overlooked; and that the mental model that underlies the manual process is inadequate. We propose to extract scenario types from real driving data by clustering recorded scenario instances, which are composed of timeseries. Existing works in the domain of traffic data either cannot cope with multivariate timeseries; are limited to one or two vehicles per scenario instance; or they use handcrafted features that are based on the mental model of the data scientist. The latter suffers from similar shortcomings as manual scenario type derivation. Our approach clusters scenario instances relying as little as possible on a mental model. As such, we consider the approach an important complement to manual scenario type derivation. It may yield scenario types overlooked by the experts, and it may provide a different segmentation of a whole set of scenarios instances into scenario types, thus overall increasing confidence in the handcrafted scenario types. We present the application of the approach to a real driving dataset.
Comparing Generalisation Using Crowd-Sourced vs Expert Labels for Galaxies Classification

Increasingly large amounts of data are available to astronomers. The Sloan Digital Sky Survey notes that manually classifying all captured galaxies would be an intractable task taking in the order of a hundred years to complete. It follows that scientists have turned to crowd-sourced labelling and machine learning, such as galaxy Zoo 2, to automate this task. If galaxy classification is to be automated in this way, then understanding how these results hold up to expert Hubble tuning classification schemes such as the academically accepted Revised Shapley-Ames catalogue of bright galaxies is timely. We use the Galaxy Zoo 2 data to train a base ResNet-50 model for transfer learning which we use to predict the Hubble types of Galaxies in the Revised Shapley-Ames catalogue. This result demonstrates how well the Galaxy Zoo 2 responses generalise to an expertly labelled catalogue of galaxies. We also train a ResNet-50 model on the EFIGI catalogue, which contains expertly labelled galaxies, and test on the Revised Shapley-Ames catalogue to draw a comparison to the crowd-sourced Galaxy Zoo 2’s performance. Our results show the effectiveness of using transfer learning for galaxy classification. Further, we reinforce that more recent architectures such as ResNet-50 improve on state of the art in this task. We also demonstrated that an expertly labelled dataset of galaxies is marginally better for training a model to predict Hubble types of galaxies than a non-expert, citizen-science labelled dataset of galaxies. However, the generalisation to the Revised Shapley-Ames is not sufficient for real-world application and further investigation is required.
Should I Disclose My Personal Data? Perspectives From Internet of Things Services

This work proposes a theoretical framework for explaining the end users’ willingness to disclose their personal information to the IoT service providers, despite the known privacy risks. The Communication Privacy Management Theory and Privacy Trust Behavioral Intention Model are used as the backbone for the presented framework. The model is empirically validated by collecting data from 924 participants residing in Thailand and Singapore who are active users of at least one type of IoT service: smart home, smart healthcare or smart cities. The results suggest that trust, perceived privacy risks, perceived benefits and the level of information sensitivity affect the users’ willingness to disclose their personal information. Certain cultural differences are also noticed from the two different country samples. Based upon the results, the research implications are discussed, and suggestions provided.
Attention R-CNN for Accident Detection

This paper addresses accident detection where we not only detect objects with classes, but also recognize their characteristic properties. More specifically, we aim at simultaneously detecting object class bounding boxes on roads and recognizing their status such as safe, dangerous, or crashed. To achieve this goal, we construct a new dataset and propose a baseline method for benchmarking the task of accident detection. We design an accident detection network, called Attention R-CNN, which consists of two streams: one is for object detection with classes and one for characteristic property computation. As an attention mechanism capturing contextual information in the scene, we integrate global contexts exploited from the scene into the stream for object detection. This introduced attention mechanism enables us to recognize object characteristic properties. Extensive experiments on the newly constructed dataset demonstrate the effectiveness of our proposed network. The dataset and source code are publicly available on our project page. 1 https://sites.google. com/view/ltnghia/research/accident-detection
Evolution of the Security Models in Cognitive Radio Networks: Challenges and Open Issues

Inclusion of Cognitive Radio Network (CRN) in different networking technologies is proven to offer an efficient communication system due to its potential characteristics of managing the transmission and reception attributes, especially in the wireless environment. CRN harnesses the capability of resisting interference between the licensed users and unlicensed user. However, it is still challenging to countermeasure the level of threats in the wireless medium by CRN. With various research-based solutions towards securing CRN, existing approaches still witness security breach when CRN is deployed over complex networking technologies. Therefore, this paper provides a technical insight in gauging the level of effectiveness in the existing security aspect and facilitates the upcoming researchers with information about the current research gap and possible solutions to leverage the security.
Changes of Antioxidant Capacity and Phenolics in Ocimum Herbs after Various Cooking Methods

Five different cooking methods - blanching, boiling, steaming, sauteing and high temperature (121°C) cooking - were applied to Ocimum herbs. Four Ocimum species - O. americanum (hairy basil), O. tenuiflorum (holy basil; syn. O. sanctum), O. basilicum (sweet basil) and O. gratissimum (wild basil) - were used to determine the effect of heating on their antioxidant capacity, total phenolic content, and presence of phytochemicals. Cooking with excess water, blanching, and boiling resulted in a reduction in both antioxidant capacity and phenolic content. HPLC chromatograms revealed that rosmarinic acid leached from the sweet basil leaves into the cooking water, in which sinapic acid was also detected. Meanwhile sauteing, as well as steaming at atmospheric and high pressures respectively, generally enhanced the antioxidant capacity of Ocimum, which was related to an increase in phenolic content. Similar chromatograms were detected in fresh, atmospheric steamed, and sauteed leaves, although the intensity varied. A major compound of the studied Ocimum herbs, rosmarinic acid, although found to increase in sauteed leaves, was substantially minimized in leaves steamed under pressure.