Miracle-dz/UniFAR-FTUs huggingface dataset:
Mass Flow Rate Measurement of Pneumatically Conveyed Solids Through Multimodal Sensing and Data-Driven Modeling

Online continuous measurement of the mass flow rate of pneumatically conveyed solids is desirable in the monitoring and optimization of a range of industrial processes, such as food processing, chemical engineering, and power generation. This article introduces a technique for the mass flow rate measurement of pneumatically conveyed solids based on multimodal sensing and data-driven modeling. The multimodal sensing system is comprised of an array of ring-shaped electrostatic sensors, four arrays of arc-shaped electrostatic sensors, and a differential-pressure (DP) transducer. Data-driven models, including artificial neural network (ANN), support vector machine (SVM), and convolutional neural network (CNN), are established through training with statistical features extracted from the postprocessed data from the sensing system. Statistical features are shortlisted based on their importance by calculating the partial mutual information (PMI) between the features and the corresponding reference mass flow rate of solids. Experimental work was conducted on a laboratory-scale rig to train and test the models on both horizontal and vertical pipelines with particle velocity ranging from 10.1 to 36.0 m/s and mass flow rate of solids from 3.2 to 35.8 g/s. Experimental results suggest that the ANN, SVM, and CNN models predict the mass flow rate of solids with a relative error within ±18%, ±14%, and ±8%, respectively, under all test conditions. The predicted mass flow rate measurements with the ANN, SVM, and CNN models are repeatable with a normalized standard deviation within 14%, 8%, and 5%, respectively, under all test conditions.
API Change Impact Analysis for Android Apps

Android has recently become one of the best platforms for mobile app development. The constant evolution of this mobile operating system results in frequent updates to its APIs, which may affect the functionality of Android apps that are built upon them. Given the high frequency of Android API updates, impact analysis plays an important role in achieving high reliability for Android apps. This paper presents Apicia, a novel approach to API change impact analysis for Android Apps. Apicia reports the impact induced when updating the target API in terms of affected program elements (i.e., classes, methods, and statements), affected tests whose executions may exhibit different behaviors due to the API update, as well as untested affected code. We evaluate Apicia on 31 real-world Android apps, and the experimental results show that it can be cost effective on regression test selection as on average only 35.31% of tests per app are affected by API update. Moreover, since many affected statements are not covered by existing tests, Apicia can assist app developers in test suite augmentation for testing these statements. These findings indicate that Apicia is a promising technique for assisting Android developers with understanding, testing, and debugging for an API update.
Prediction of COVID-19 from Chest X-ray Images Using Multiresolution Texture Classification with Robust Local Features

The COVID-19 contagious disease that spread around the world, have a huge risk on people and already caused millions of deaths forcing a global pandemic in 2020. Diagnosing patients with this disease is very critical allowing fast care response and to isolate them from public. As the virus spread widely to millions of people, the fastest way to detect it is by analyzing radiology images. Early studies showed irregularity in the chest X-ray images of patients with high clinical belief of COVID-19 infection. Hence, these studies motivated us to investigate the use of machine learning techniques to help diagnosing COVID-19 patients from chest CT scans. In this paper, we propose to use a robust feature extraction descriptor and to apply a Random Forests classifier to predict COVID-19 disease in a dataset of 5000 images. First, 408 texture features are extracted using a powerful variation of Local Binary Patterns descriptor called Rotation Invariant Co-occurrence among Local Binary Patterns. Then, Random Forests classifier is applied with 250 trees to perform the classification task. Moreover, the performance of our approach was improved by using a multiresolution scheme where features are extracted from both the original input image and the subsampled image. Two metrics were used to evaluate our approach, sensitivity and specificity. We achieved 99.0% and 91.3% for both metrics, respectively. Our results are close to the state-of-the-art deep learning methods on the same dataset.
Neuroadaptive Observer-Based Fault-Diagnosis and Fault-Tolerant Control for Quadrotor UAV

This paper highlights the design of an adaptive observer based on radial basis function neural network (RBFNN) for the diagnosis of actuator faults in a quadrotor UAV (Unmanned Aerial Vehicle), and the design of a fault-tolerant control system. First, the quadrotor UAV model considering the faults and unknown disturbances is presented. Next, the adaptive observer design is implemented using a reduced order model of the system, in order to simplify the structure of the neural network and consequently reduce the computational complexity and facilitate the adjustment of its parameters. The fault-tolerant control system is then exposed through the combination of the controller and the faults estimated by the adaptive observer. Simulations are performed in order to demonstrate the effectiveness of the proposed system with respect to the speed and accuracy of faults estimation with nonlinear behaviors and simultaneous occurrences in the actuators, and the attenuation of the effects of faults on the quadrotor UAV dynamics.
The Latency Characteristics of GTP-U and SRv6 Stateless Translation on VPP Software Router

The software switches and routers are rapidly developed in the NFV/SDN area, and are expected to be adopted in the emerging future mobile network (beyond-5G). Despite the high expectation of software routers, its latency characteristics have not been investigated extensively. To provide the reference latency value for software-based network function, we analyzed the latency of SRv6 Mobile User Plane on VPP software router. We show that, 1) for the beyond-5G mobile network application, the latency is in the feasible range (roughly 3–30 μs), 2) the throughput is sufficient with the range in 1–5 Mpps per CPU core, 3) the latency has the strong correlation with the input traffic load, 4) and the latency can be stable in a carefully crafted implementation. The packet interval analysis shows the burstiness of its output traffic that is enforced by the processing of a vector (bulk/batch) of packets. Our latency characteristic analysis can be of help for improving the performance of software router using the batch processing and designing software-based beyond-5G mobile communication systems.
A Multi-case Perspective Analytical Framework for Discovering Human Daily Behavior from Sensors using Process Mining

Due to the rapid development of sensor technology, wearable sensors have been widely applied in various real-life human applications and improved the mass adoption of smart environments. This recent technology offers a pioneering opportunity to recognize human daily behavior patterns from a large amount of collected data. In this work, we address the challenge of applying process mining to discover human daily behavior patterns from sensors. Sensor data could be seen as the execution of a process representing user daily activity. However, it requires additional tasks for investigating the perspective of sensor data that comes from diverse environmental settings. Therefore, we propose an analytical framework to discover human daily behavior patterns from various sensors in a smart environment. In order to evaluate the proposed framework, a real-world dataset in a smart environment is used. From the conducted experiment, this framework could be used to transform general human activity data and takes into account the process mining to discover human daily behavior in accordance with a multi-case perspective process model (i.e., user-based, time-based, and sensor flow pattern).
The Design of Pixelated SIW Horn Antennas With Nearly Equal Beamwidths Using Binary Ink Stamp Optimization

In this paper, linearly and circularly polarized single-layer Substrate Integrated Waveguide (SIW) horn antennas with nearly equal half power beamwidths in principal planes for the 24 GHz ISM band are designed. The uniqueness of the solution lies in the application of a pixelated structure to the design of these SIW horn antennas. For the design, a novel heuristic algorithm called Binary Ink Stamp Optimization is proposed. At first, the ability of the proposed algorithm is proved over fifteen benchmark functions and compared with the results of six state-of-the-art optimization algorithms. Then, the novel algorithm is exploited for the design of the pixelated part of the antennas. The antennas are manufactured using low-cost 3D printing technology. The experimental results prove that the linearly polarized antenna has a reflection coefficient below −18 dB in the whole 24 GHz ISM band. At the center frequency of this band, the antenna gain is 14.2 dBi, and the level of side lobes is below −15 dB in E and H-planes. In addition, E and H-plane half power beamwidths are 26.5° and 25°, respectively. The circularly polarized antenna radiates a right-hand circularly polarized (RHCP) wave, the reflection coefficient, and axial ratio are below −12 dB and 3 dB in the whole 24 GHz ISM band, respectively. At the center frequency of this band, the antenna gain is 8.9 dBi and the level of side lobes is below −7.4 dB in both principal planes. Further, the half power beamwidths in principal planes are 30.6° and 33.4°, respectively.
Social-based City Reconstruction Planning in case of natural disasters: a Reinforcement Learning Approach

Natural disasters always have several effects on human lives. It is always challenging for governments to tackle these incidents and to rebuild the economic, social and physical infrastructures and facilities with the available resources (mainly budget and time). The governments always define plans and policies in accordance with the law and political strategies that should maximize social benefits. The severity of damage and the huge resources needed to bring back life to normality make such reconstruction challenging. This article presents an approach to decision-support system by using deep reinforcement learning technique for the planning of post-disaster city reconstruction by considering available resources, meeting the needs of the broad community stakeholders (like citizens’ social benefits and politicians’ priorities) and keeping in consideration city’s structural constraints (like dependencies among roads and buildings). The proposed approach post disaster Rebuilding Plan Provider (pd-RPP) is generic, can determine a set of alternative plans for local administrators who select the ideal one to implement and it can be applied to areas of any extension. We show the proposed approach on a district of Sulmona city in Italy.
What should I learn next? Ranking Educational Resources

Artificial Intelligence tools can enable a better use of Open Educational Resources (OER) by making it easier to search for OER or to receive high quality recommendations. Ideally, a user should learn from resources suggested in some order consistent with what a teacher may call "progressive". The progress of natural language processing techniques over the recent years allows us to propose a timeline-preserving neural network model whose goal is to be able to rank resources. We experiment with series of lectures organized in courses made by teachers as ground truth. Our method is able to rank correctly 80% of pairs of lectures with a contextual background and 69% in an agnostic setting. Our contribution is completed by the formalization of this task as a machine learning problem, and by the distribution of a new free open data-set.
Non-Isolated DC-DC Ćuk-Buck Converter for High Step-Down Applications

This work proposes a non-isolated single-switch dc-dc converter based on the integration of the classical buck and Ćuk topologies using the graft technique. The resulting converter aggregates some interesting advantages, e.g., both the input current and the current through the output stage are continuous with low ripple. A qualitative and quantitative analysis of the Ćuk-buck converter is derived, showing that the circuit is suitable for high step-down applications. Simulation results obtained with PSIM® software are presented and discussed to demonstrate the accuracy of the theoretical analysis.
A New Framework and Methodology for Digital Twin Development

Digital Twins can be defined as virtual copies of physical assets that interact in real time with their physical counterparts. It is rated as one of the most promising technologies in the Industry 4.0 context, but there are still many gaps to be filled for this technology to become effective and viable. This paper proposes a development methodology and a framework for the implementation of manufacturing process’ digital twins. The concept of Unit Manufacturing Process, that is the smaller part of an industrial process that can perform a significant changing of the raw material, and the modularity it brings to build digital twins facilitate the digitalization of an entire manufacturing process. The methodology was created to surpass generalization and scalability problems and can be applied to discrete, continuous and batch processes. Two digital twins of manufacturing processes are being developed to serve as testbeds for the methodology. The digital twin concept can be a very supportive tool to enhance productivity.
Multi-faceted Classification for the Identification of Informative Communications during Crises: Case of COVID-19

Social media data are used to enhance crisis management, as people widely adopt social media to share and acquire information to cope with uncertainties in crises. Identification and extraction of informative communications out of large volumes of data is critical for accurate situational awareness and timely response. Existing studies use conditions of geolocations, keywords, and topics separately or jointly to retrieve data that can be crisis related, but are not enough to filter subsets of data for different crisis management tasks. We propose that the crisis communication purposes of users can be detected to enhance data selection and prioritization for different crisis management tasks. A classification framework was built to identify three facets of a message: content type, audience type, and information source. The definitions of these categories are not dependent on a specific type of crises. So the classification framework can be potentially applied to different crisis scenarios. Machine learning models were created for the automatic classification of messages. Results showed the CNN-based model achieved the best accuracy (88.5%) for the classification of content type. The proposed Naive Bayes and logistic repression with predetermined features can best differentiate audience types and information source with an accuracy of 72.7% and 72.2%, respectively.
The Influence of the Emission Source on Outcoupling and Directivity of Patterned Perovskite Light-Emitting Diodes

The perovskite light-emitting diodes (PeLEDs) have attracted considerable research interests in recent years due to their decent and tunable optoelectronic characteristics. Here, we unveil the properties of the emission source (e.g., location, polarization, etc.) and the influences of the incorporated nanopatterns on the light emission of PeLEDs by using the finite difference time domain (FDTD) method. The results indicated that the PeLEDs with nanopatterns not only show substantial improvement of light extraction intensity but also reveal a remarkable directivity. Moreover, the emission angle of the directional PeLEDs can be continuously engineered over 65o by judicious selection of the nanopatterns. This work is of great importance for engineering highly directional and efficient luminescence devices.
Unmasking the Mask Debate on Social Media

Masks are believed to slow the spread of Covid-19, and can prevent many deaths, yet this inexpensive, common sense public health measure has ignited a fierce debate in the U.S. Opponents of masks or anti-maskers have resorted to measures such as organizing protests and marches to make their views public. They have also taken to social media platforms to vigorously argue against the use of masks, and spread misinformation, lies, and myths regarding their use. Even with the advent of vaccines, masks are still likely to be recommended for a long time. It is therefore necessary to identify those tweets that spread falsehoods regarding the use and effectiveness of masks in order to limit their appeal and damage. This paper proposes a classification framework to detect anti-mask tweets from social media dialogue shared on Twitter during the months of July and August 2020. The framework relies on popular machine learning models trained using a combination of linguistic, auxiliary, psycho-linguistic and sentiment features for detection. The proposed classification framework can detect anti-mask tweets with excellent accuracy of over 90%, and hence, it can be used to tag tweets that sow misinformation about masks before they spread through the ether and influence people.
Singularity Free Stabilizing Controller for Non-Holonomic Mobile Robot Using Novel Sliding Manifold

This work proposes a singularity free stabilizing controller for a non-holonomic mobile robot. The control objective is to force the system states from any initial position to the origin in finite time. In the first step the state transformation has been done on the kinematic and dynamic model of the mobile robot. After transformation the kinematic and dynamic model has been converted to non-holonomic integrator and non-holonomic double integrator respectively. Further a novel sliding surface has been proposed in the paper which stabilizes both non-holonomic integrators in finite time. Kinematic stabilization has been done using power rate reaching law of sliding mode controller (SMC) and dynamic stabilization of mobile robot has been done using relay free SMC technique. Stability analysis of the proposed kinematic and dynamic controller has been given using Lyapunov stability theory. Simulations studies are performed, and it is shown that the proposed controller is better than the existing state of the art.
SRM Analysis for Undergratuate Courses

This work presents a didactic tool for the analysis of switched reluctance motors (SRM) using the finite element method (FEM) and using platforms known and used by undergraduate students as the Finite Element Method (FEMM). The presented method simplifies the observation of magnetic fields, inductances, fluxes and torques and makes the teaching of electromagnetism, electrical machines and power electronics more attractive. Simulation examples applied to SRM type 8/6 and their results such as inductance, torque and flow are presented and discussed.
HLSTM: Heterogeneous Long Short-Term Memory Network for Large-Scale InSAR Ground Subsidence Prediction

Accurate prediction of ground subsidence is of great significance for the prevention and mitigation of this type of geological disaster. It is still a challenge when wide area is concerned. In this article, a heterogeneous long short-term memory (HLSTM) network is proposed for large-scale ground subsidence prediction based on interferometric synthetic aperture radar (InSAR) data. First, the study area is divided into homogeneous subregions through spatial clustering of InSAR-derived subsidence velocity. Second, a specific LSTM model is constructed to capture complex nonlinear temporal correlations embedded in InSAR-derived subsidence time series for each subregion. Essentially both spatial heterogeneity and temporal correlation are incorporated into the HLSTM prediction. In the experiment part, the HLSTM predictor is validated using a subsidence monitoring result from 80 Sentinel-1 images acquired over Cangzhou, China, from 2017 to 2019. The HLSTM result shows the highest prediction accuracy through comparisons with the results from other seven methods.
Metamorphic Testing for Block Ciphers

Information is indispensable in modern society. People’s daily communication and work depend on information transmission. Unsafe storage or transmission of data may result in privacy and security problems. One way to attempt to prevent such issues is to use encryption algorithms to transform information into encrypted forms. Because the encryption steps of most encryption algorithms are complex, deciding the correctness of the encrypted output may take a long time in practice. This kind of problem is called the Test Oracle problem. In contrast to traditional software testing, Metamorphic Testing (MT) does not focus on the correctness of each individual output, but examines whether the inputs and outputs of multiple executions of a Program Under Test (PUT) satisfy necessary relations of the PUT, called metamorphic relations. This paper reports on an experience of applying MT to test three encryption algorithms — Data Encryption Standard (DES), Triple Data Encryption Standard (3DES), and Advanced Encryption Standard (AES).
Diagnostic Imaging Support System for Rheumatoid Arthritis Using Ultrasound Images

In recent years, image inspections have become a useful tool in the early diagnosis of rheumatoid arthritis (RA). However, evaluations in RA ultrasound inspections are visual inspections by doctors, evaluation tends to become somewhat subjective, meaning that it is difficult for less-experienced doctors to make an assessment. In this study, therefore, we propose a system that alleviates the burden on doctors, by automating RA image diagnosis, that is to say an RA image diagnosis support system using ultrasound images. With this system, as it is necessary to automate RA image diagnosis, it is necessary to propose an image classification method with higher classification accuracy. Therefore, in this paper, we propose an image classification method with higher precision, by comparing methods of image classification. We also show our evaluation results. As a result, it is considered that image classification via CNN using automatic extraction images of synovial thickening surrounding areas is effective for image classification of ultrasound images for the purpose of automating RA image diagnosis.
Shared Control for a Handheld Orthopedic Surgical Robot

A handheld robot with embedded parallel kinematic manipulator (PKM) is of compact size and is able to provide a similar workflow and exhibit great usability in robot-assisted surgery. The handheld robot is particularly advantageous for tremor suppression and motion stabilization, but also suffers from the issues of the actuators reaching the limit of their range of motion and the reflected force artifact from the actuators. These two pitfalls could make the handheld robot produce intermittent pause or jerky force during human operation. In this paper, a shared control with a handle-based navigation human-machine interface (HMI) has been proposed. A guiding pose region was designed to meet both the indices of dexterity maximization and kinetic energy minimization of the PKM. The control of the handheld robot is shared by the human for holding the handle entering the guiding pose region and by the PKM for positioning the tool and eliminating human tremor. The experimental results presented that the handheld robot under shared control with handle-based HMI could achieve an improvement both in tool tracking accuracy and force artifact reduction without intermittent pause than the counterpart which is under cooperative control with the traditional tool-based HMI.