Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimation of Test Scores Based on Video Viewing Behavior in the Programming MOOC Course

The massive open online courses (MOOCs) are expected to offer learning opportunities to various type of learners with different backgrounds. However, MOOCs have many learners who drop out during learning process, and the completion rates are as low as about 10%. In order to improve such a drawback, it is necessary to grasp the features of the learners in the earlier stage and to provide appropriate supports to each learner. This paper estimates the difference of learners’ behavior by investigating relationship between video viewing logs and test scores in the programming MOOC course. It was observed that the repeated learning relates the higher score, the later learning relates the lower score. Even in learners who got final score between 20 to 70, there are possible learners who could be rescued by offering appropriate supports. This cluster of learners was also visible in the multiple regression analysis.
A 3-D Reconfigurable Memory I/O Interface Using a Quad-Band Interconnect

This article presents a 3-D reconfigurable memory I/O transceiver using a quad-band interconnect (QBI). The 3-D QBI provides I/O data reconfigurability, decreases latency, and reduces pin count for future compact mobile memory interfaces. The 3-D integrated circuit (3-D IC) technique is utilized to reduce signal latency and improve signal integrity. A novel quad-band transformer is proposed to achieve reconfigurable four-band data communication and reduce the I/O pin count by four times. A two-tier QBI die-stack is implemented to verify the QBI design. Face-to-face configuration with <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>bump interconnects is used to save cost. The QBI chips are designed and fabricated in a 180-nm CMOS process. The chip areas of the top and bottom dies are 1.77 and 1.4 mm<sup>2</sup>, respectively. The measured data rates, with bit error rate (BER) <inline-formula> <tex-math notation="LaTeX">$< 10^{-15}$ </tex-math></inline-formula>, are up to 2 Gb/s in the baseband (BB) and 2.3, 2.5, and 3 Gb/s in RF-bands. The QBI energy efficiencies, with a 1.8-V supply voltage, are 5.9 pJ/b in the BB and 6.2, 7.4, and 8 pJ/b in the RF-bands.
RFID Sensing Technologies for Smart Agriculture

With the advent of emerging technologies, traditional agriculture practices are now shifting towards the “smart” approach. Smart agriculture aims to automate most aspects of the plant growing process to save the labor and improve the efficiency, the yield and quality of the crops/harvest. In recent years, Radio Frequency Identification (RFID) sensing technology has emerged and unleashed a new series of applications in the field of agriculture. This paper first introduces RFID sensing technology, then surveys its applications to the four areas of agriculture: plant growing environment, soil conditions, plant growth, and the quality of the harvests. The paper aims to identify the uniqueness of RFID sensing technology in solving agriculture issues and its limitations.
Variable-Bandwidth Model and Capacity Analysis for Aerial Communications in the Terahertz Band

In this work, 0.75-10 THz band is explored for non-terrestrial communications, by leveraging the improved atmospheric conditions at various altitudes. A channel model for aerial communications at THz band is proposed to calculate the common flat bands for frequency-selective path gain and the colored noise spectrums, both of which are highly affected by the atmospheric conditions. With capacity computation based on common flat bands, we consider aerial vehicles at different altitudes and distances, under realistic weather and channel fading conditions, due to beam misalignment and also multi path fading. An extensive capacity analysis is presented, considering equal and water-filling power allocation. It is shown that, when there is no fading, capacity for aerial links is several orders of magnitude larger than the sea-level capacity. When ergodic capacity is computed for the fading scenarios, it is shown that the impact of fading vanishes with altitude. Sea-level ergodic capacity is increased by an order of magnitude for drone-to-drone communications, providing several Tbps at 10 m range, while 10s of Tbps is achievable among jet planes and UAVs, and several 100s of Tbps is possible for satellites/cubesats at 1 km under fading, suggesting that THz band is a promising alternative for aerial communications.
Application Framework for Semantic Web of Things

Application framework is an alternative to achieve a simpler and easier way to build Semantic Web of Things Applications. Application framework is using JSON as a configuration file and owl as a converter file as an input, then application framework will aggregate data using C-SPARQL and inference with SWRL to produce some output. The application framework gives functionality to do webhook and streams using Kafka as an output. The framework successfully reduces time consumption and reduces the line of code to build a semantic web of things without losing any functionality.
Total-Ionizing-Dose Response of Highly Scaled Gate-All-Around Si Nanowire CMOS Transistors

Gate-all-around (GAA) silicon nanowire (NW) CMOS transistors demonstrate outstanding total-ionizing-dose (TID) tolerance due to the ultrascaled gate dielectric thickness, enhanced electrostatic gate control, and suppression of parasitic leakage current paths. nFETs and pFETs show similar TID responses, making the GAA NW technology an excellent candidate for CMOS IC applications in high-radiation environments. The slight degradation of the threshold voltage suggests limited charge buildup in the gate dielectrics. However, low-frequency noise and random telegraph noise measurements show the importance of change in trap configurations in both the near-interfacial SiO2 and HfO2 dielectric layers to the radiation response and reliability of GAA NW devices. These traps are most likely due to oxygen vacancies and/or hydrogen complexes.
Total Ionizing Dose Effects on Multistate HfOₓ-Based RRAM Synaptic Array

Emerging nonvolatile memories (eNVMs) have demonstrated satisfactory accuracy on various applications in deep learning. Characterized by high density and low leakage power consumption, resistive random access memory (RRAM) becomes very attractive in synaptic devices for deep neural networks (DNNs). RRAM-based synaptic devices include both analog and discrete versions. Unlike analog RRAM synapses which suffer from nonlinearity, discrete but multistate RRAM synapses are better suited for neural network hardware implementation. In this article, the multistate operation in RRAM arrays has been proposed as a synaptic device for DNN inference. Four-state conductance has been achieved in HfO<sub><italic>x</italic></sub>-based RRAM synaptic arrays. The impact of total ionizing dose (TID) on the multistate behavior of HfO<sub><italic>x</italic></sub>-based RRAM is investigated by irradiating a one-transistor–one-resistor (1T1R) 64-kb array with CMOS peripheral decoding circuitry fabricated at the 90-nm technology node with Co-60 gamma rays (<sup>60</sup>Co <inline-formula> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula>-ray irradiation).
Design Exploration of ReRAM-Based Crossbar for AI Inference

ReRAM-based crossbar designs utilizing mixed-signal implementation has gained importance due to their low power, small size, low cost, and high throughput especially for multiply-and-add operations in AI-related applications. This paper provides a framework with associated code for analyzing the impact of ReRAM device variation and post-training analog conductance quantization that has not been fully explored for pre-trained network accelerators. A detailed study with end-to-end implementation is presented, ranging from mapping the pre-trained DNN weights to quantized crossbar conductance values and into final classification with the presence of variation. Monte Carlo analysis was performed to better analyze the impact of the different parameters on the final accuracy without being device-specific. The work assumes different conductance value variations, different conductance dynamic ranges, and various device quantization levels (QLs). MNIST and CIFAR-10 data sets were used in this study for ANN and CNN, respectively. Results show that for simple ANN, the accuracy drop due to quantization was ~ 2% at 64-QLs. While for CNN, the decrease in classification accuracy was around 10% with the same number of levels. Moreover, weight variation might cause a ~ 5% and ~ 8% drop in classification accuracy for ANN with 5% and CNN with 3% variation, respectively. The study confirms that increasing the number of levels with small variation results in near-optimal accuracy. However, the increase in accuracy saturates at an upper limit. The amount of distortion propagated through the layers is different in the two cases. It is dependent on the complexity of input data and network structure, such as the size of the neurons in each layer, the number of layers, and the number of channels and filters at each stage. This contribution is the first to provide the framework to explore the implication that emphasizes on device-independent post-training DNN quantization and weight variation on classification accuracy. This helps explore design trade-offs especially for edge devices in cases where there is no access to the training set to the end-user due to security or cost issues for pre-trained networks.
Soft Gripper for Robotic Harvesting in Precision Agriculture Applications

Soft technologies are called to produce a significant advance in the manipulation of delicate objects. A field that is presented as especially attractive for its application is the automation of the selective harvesting of high value crops. This article addresses the design of a soft gripper and its adaptation to a dual-arm robotic system for precision agriculture tasks. The experimental tests carried out demonstrate the feasibility and effectiveness of the proposed prototype.
Prior Mask R-CNN Based on Graph Cuts Loss and Size Input for Precipitation Measurement

Fusing prior knowledge with data-driven deep learning for measurement is interesting and challenging. For the detection of metallographic precipitations, the measurements of size and shape of precipitations are roughly predictable in advance through a transmission electron microscope (TEM). In this article, we proposed a novel instance segmentation network named prior mask R-CNN by fusing prior knowledge for automatic precipitation detection. On the basis of the typical mask R-CNN framework, we made the following improvements. First, at the bounding box classification stage, in order to restore area information, we input the size information besides only uniform dimension features after the region of interest align (RoIAlign). Second, at the mask segmentation stage, we proposed a new loss function based on normalized graph cuts. It is category-sensitive by setting different weight strategies for different categories based on their prior shapes. In addition, from the point of view of practicality, we designed an effective measurement extraction module to get specific measurements, such as the length of precipitations, from the final prediction results of our network. In a variety of experiments, our method achieves the highest mean average precision (mAP) of 0.475 and 0.298 among different famous methods for bounding box detection and mask segmentation tasks, respectively, which proves the effectiveness of our method.
Simulation of a Robotic Co-transport System

The transportation of large loads is still an obstacle to the productivity of some companies. To answer this internal logistics challenge, one solution with high potential is the use of several mobile robots that can execute these tasks cooperatively. This paper addresses the simulation of strategies that allow two omnidirectional robots, a leader and a follower, to coordinate their movement and cooperate in a transportation task (co-transport). This is achieved by implementing a force sensor on the mechanical structure of a support that allows the follower to grab and transport the load on the top of its chassis. Through the measurement of the forces, this robot will be able to react to the movement of the leader, whose function will be to guide the system to its final destination. The system and its cooperation strategies were idealized and then tested in the CoppeliaSim simulator. The results obtained through simulation were the target of an analysis that assessed which strategy best complemented the omnidirectional mobility of the system’s constituents and, at the same time, ensured that less forces were exerted on the load during transport.
Customer Complaints Clusterization of Government Drinking Water Company on Social Media Twitter using Text Mining

Social media is considered one of the most effective platforms to communicate between companies and customers. Frequently, the customer of a product or service sends complaints via social media. Customers’ complaint data serve as a good suggestion for companies and organizations to improve their products and services. With the increasing number of customer complaints that have entered through social media accounts, government-owned drinking water companies need a more efficient way to extract information from complaint data. In this research, text mining is used to extract information about customer complaints against drinking water companies from social media Twitter. Latent Dirichlet Allocation (LDA) and self-organizing maps (SOM) approach is applied to model complaint topics and find out which are most frequently complained. The test results indicate grouping the data into five classes is the most appropriate model. Pipes leakage are the most frequently reported topics, 27.8% of total datasets.
Microwave-assisted synthesis and photocatalytic properties of sulphur and platinum modified TiO2 nanofibers

In the present work formation of active TiO2 nanofibers in microwave synthesis and their modification with platinum were studied. Mixture of anatase and rutile nanopowder and 10M KOH solution were used as raw materials. Microwave assisted synthesis method permitted to obtain TiO2 nanofibres with a diameter of 10nm and a specific surface area up to 40.2 m2/g. In order to modify TiO2 nanofibers with platinum it was stirred in H2PtCl6 solution and illuminated with UV irradiation or reduced with sodium boronhydride. To modify titania with sulphur and prepare co-doped nanofibers platinum doped samples were extra treated in hydrogen sulphide atmosphere. Photocatalytic activity was determined by degradation of the methylene blue (MB) solution under UV and visible light irradiation. The obtained samples showed higher photocatalytic activity with respect to pure TiO2 nanofibers. The doped TiO2 nanofibers were appropriate for degradation of harmful organic compounds.
Liquid Chromatographic Determination of Patulin in French Apple Ciders

A liquid chromatographic (LC) method was developed for detection and quantitation of patulin in French apple ciders. Cider was applied to an Extrelut column; patulin was extracted with ethyl acetate-toluene, and the extract was cleaned up on a Sep-Pak Florisil cartridge. Patulin was detected with a UV photometer at 275 nm. A reversed-phase column and an elution gradient were used for LC separation. Patulin recoveries at 3 levels ranged from 90.4 to 91.7%. Repeatability at 50 and 100 ppb levels did not exceed 15%. The calibration graph was rectilinear from 0.2 to 1 μg/mL patulin working solutions corresponding to 20-100 ppb patulin in sample. The quantitation limit was 10 ppb. The method represents an improvement in selectivity for determination of patulin in a specific matrix such as French apple cider.
Evolution of States in Single Electron Tunneling Circuits

This paper presents a numerical solution of the dynamic Master Equation that governs the evolution of states in single electronic systems. The solution presents the ensemble average occupancy of states during the transient stage and at the steady-state. It is shown that the settling time of the circuits resulting from a change in bias conditions depends on the initial and final bias conditions. The asymmetrical settling times must be taken into consideration for the design of digital and analogue single electronic circuits
Complementary Photoplethysmogram Synthesis From Electrocardiogram Using Generative Adversarial Network

Photoplethysmogram (PPG) is one of the most widely measured biosignals alongside electrocardiogram (ECG). Due to the simplicity of measurement and the advent of wearable devices, there have been growing interest in using PPG for a variety of healthcare applications such as cardiac function estimation. However, unlike ECG, there are not many large databases available for clinically significant analyses of PPG. To overcome this issue, a Generative Adversarial Network-based model to generate PPG using ECG as input is proposed. The network was trained using a large open database of biosignals measured from surgical patients and was externally validated using an alternative database sourced from another hospital. The generated PPG was compared with the reference PPG using percent root mean square difference (PRD) and Pearson correlation coefficient to evaluate the morphological similarity. Additionally, heart rate measured from the reference ECG, reference PPG, and generated PPG, and compared through repeated measure analysis of variance to test for any significant differences. The mean PRD was 32± 10% and the mean correlation coefficient was 0.95± 0.05 in the test dataset. The HR from the three biosignals showed no significant difference with a $p$ -value of 0.473. When the optimized GAN model was tested on atrial fibrillation ECG from a third dataset, the mean correlation coefficient between the generated PPG heart rate and the ECG heart rate was 0.94± 0.15, with paired t-test resulting in $p$ -value of 0.64. The results indicate that the proposed method may provide a valuable alternative to augmenting biosignal databases that are abundant in one signal while lacking in another.
Application of Deep Learning for Early Detection of COVID-19 Using CT-Scan Images

COVID-19 pandemic caused a vast impact worldwide. The imbalance between the number of tools for COVID-19 detection and the demand for COVID-19 tests from citizens has overwhelmed the government. To overcome this problem, artificial intelligence is utilized, specifically in the deep learning field. In this paper, we propose FJCovNet, a new deep learning model based on DenseNet121. FJCovNet managed to get an accuracy of 98.14%, surpassing Xception with an accuracy of 84,24%, VGG19 with an accuracy of 95.25%, and ResNet50 with accuracy of 91.53%. FJCovNet also managed to get less training time with 612 seconds, lesser than VGG19 with 808 seconds and ResNet50 with 809 seconds, and only slightly more than Xception with 609 seconds.
Energy, Economic, and Environmental (3E) Analysis of Zero Energy Consumption Building: A Case Study of Thai Style Mediation House

This paper presents Energy, Economic, and Environmental (3E) analysis of Zero Energy Consumption Building (ZECB) concept. A case study of Thai mediation house which constructed following ZECB concept is provided. The conditions of this off-grid PV house are daily load demand is approximately 4.605 kWh and this house must be able to use the energy for 2 days without the sunlight. Each parameter in 3E analysis is used to evaluate the possibility of ZECB.
Integrator-Less Method for Phase Angle Estimation of Fundamental Frequency Positive-Sequence Component Under Adverse Condition

This article proposes an integrator-less method for phase angle estimation of unbalanced three-phase voltage systems under adverse conditions. It mainly unites abc-to-dq transform, delayed signal cancellation scheme, and a phase error compensation algorithm. It does not demand online operation of integrators, and thus, associated discretization error and instability issue are not present in the proposed method. It is also an unconditionally stable technique as it uses a real open-loop structure. Moreover, the method is relatively simple and computationally efficient due to circumventing online operations of the complex functions, such as square root, trigonometric, and inverse trigonometric. Under alike transient time conditions, the proposed method is more resistant to harmonic disturbances when compared to integrator-based similar competing techniques, such as an enhanced frequency adaptive demodulation approach and a phase error compensation-based open-loop method. The usefulness of the offered method is proved using simulated and experimental results.
Passivity Guaranteed Dynamic Friction Model With Temperature and Load Correction: Modeling and Compensation for Collaborative Industrial Robot

In this paper, a new comprehensive dynamic friction model for a collaborative industrial robot joint that considers the velocity, temperature, and load torque is proposed. The variation of load-dependent friction among the four-quadrant operation depending on the sign of load-torque and speed is studied. The new model’s passivity property is analyzed to obtain a physically meaningful and experimentally identified friction model. A sufficient condition is presented in terms of a simple algebraic inequality involving the parameters of the model. The model parameter identification procedure and validation of model effectiveness are demonstrated experimentally on a commercial collaborative robot manipulator. Moreover, the proposed friction model’s benefits are demonstrated in two different robot applications: friction compensation and direct teaching (smooth lead-through programming) applications. Significant tracking performance improvement in the root-mean-square errors up to 76% was achieved with the proposed friction model compared to the uncompensated cases in the friction compensation application. In the direct teaching application, the new model, which precisely estimates joint friction, results in a significant decrease in interaction forces up to 66%. These experimental results validate the performance of the proposed friction model.
Genre Analysis based on Principal Component Analysis and Random Forest

When you listen to music, have you ever noticed that some songs are similar to the other while some are quite different? Maybe this is a universal phenomenon, however, our work try to provide fascinating insights into the characteristics and development of music. We create the adjacency matrix of 5603 musical artists to demonstrate the relationship between them, then we draw a subnetwork to show the connection between the influencers and the followers of Classical music in the 1930s. After that, we try to quantify the influences and similarities between these artists, using the adjacency matrix and principal component analysis (PCA) method, and establishing two indicators: ‘influence’ and ‘dissimilarities’. To test the practicability of the previous indicators, we apply them to analyze the genres of music, including the a comparison of influences and similarities between and within genres, the developing process of genres and the relationship of different genres. Especially, we use the random forest method to work out what distinguishes a genre. We find that artists within genre are statitically more similar to artists between genre.