Miracle-dz/UniFAR-FTUs huggingface dataset:
A Strategy for Retrospective Evaluation of Students SQL Learning Engagements

The adoption of e-learning pedagogy in educational institutions has become increasingly prevalent, and it has proffered solutions to notable deficiencies that existed in the traditional learning pedagogy. However, it has also opened up new sets of challenges. One such challenge is the management of the huge amounts of data generated by students in the course of their daily interaction with various e-learning subsystems. Furthermore, as these systems are mostly online assessment systems, In-depth evaluation of student’s interaction is mostly coarse, and a strategy for retrospective evaluation of students’ interaction data for improved knowledge acquisition is mostly lacking. In our own peculiar scenario, an understanding of the challenges experienced by students in their pursuit of Structured Query Language knowledge acquisition was necessary. A strategy we adopted to tackle the challenge of gaining retrospective insight into student’s learning engagements is the use of error codes, and a real-time analytics. Thus, we are able to understand the extent to which our teaching strategy is effective against reducing stress resulting in exercise task engagement, and other learning challenges that may affect knowledge acquisition by individual students in the course of their learning interactions. We show that the error code strategy is an effective indicator for tracking, and intervening on student’s Structured Query Language (SQL) based learning interactions. Our evaluation further showed that syntax errors are the most prevalent type of error encountered by students learning SQL.
A Microstrip Magnetic Dipole Antenna with Reconfigurable Beamwidth

This paper presents a novel microstrip magnetic dipole (MD) antenna with reconfigurable beamwidth. The proposed design is composed of a microstrip magnetic antenna and two metal planes. The angle of the metal planes is adjustable, and the metal planes are used as a reconfigurable corner reflector. As the angle varies, the reflection coefficients in the operating band (5.67-5.91GHz) are retained below −10dB, and the E-plane half-power beamwidth (HPBW) varies from 37° to 142°. The maximum gain of 12.1 dBi is obtained when the beamwidth is 37°, and the minimum gain of 8.9 dBi is obtained when the beamwidth is 142°. The proposed design is a good candidate for base station applications to enhance traffic capacity.
Performance Enhanced Millimeter-Wave Array Antenna by Sequentially Rotating Wideband Circularly Polarized Radiating Elements

A performance enhanced circularly polarized (CP) 2×2 array antenna is constructed by sequentially rotating four wideband CP radiating elements. It is a multilayered structure based on standard printed circuit board (PCB) processing. A microstrip feeding network is designed to provide the required excitation phases and amplitudes for the four radiating elements in a wide operating band. According to the simulated results, the proposed low-profile wide-band antenna exhibits an impedance bandwidth within $25\sim 47.96$ GHz, an axial ratio bandwidth within $25\sim 46.16$ GHz and an average in-band gain higher than 5dBic, etc, thus, making it an attractive candidate for the future wireless communications.
Attention Based Multi-Agent Reinforcement Learning for Demand Response in Grid-Responsive Buildings

Integrating renewable energy resources and deploying energy management devices offer great opportunities to develop autonomous energy management systems in grid-responsive buildings. Demand response can promote enhancing demand flexibility and energy efficiency while reducing consumer costs. In this work, we propose a novel multi-agent deep reinforcement learning (MADRL) based approach to utilize real-time system information to facilitate demand response programs for minimizing electricity costs and efficient load shaping. Achieving real-time autonomous demand response in networks of buildings is challenging due to uncertain system parameters, the dynamic market price, and complex coupled operational constraints. To develop a scalable approach for automated demand response in networks of interconnected buildings, coordination between buildings is necessary to ensure demand flexibility and the grid's stability. We propose a MADRL technique that utilizes an actor-critic algorithm incorporating shared attention mechanism to enable effective real-time coordinated demand response in grid-responsive buildings. The presented case studies demonstrate the ability of the proposed MADRL approach to obtain decentralized cooperative policies without knowledge of building energy systems. The viability of the proposed control approach is also demonstrated by a reduction of over 6% net load demand compared to state-of-the-art reinforcement learning approaches.
Modulation Recognition Method of Underwater Acoustic Signal Based on Parallel Network

In underwater non-cooperative communication systems, efficient and accurate identification of underwater acoustic signal modulation is a prerequisite for stable and reliable communication. However, the underwater environment is complex and changeable, and various noises seriously interfere with the correct identification of the modulation mode of underwater acoustic signals at the receiver. A parallel neural network model is proposed in this paper based on the Encoder module and convolutional module, which fuses the convolutional network and the attention mechanism. In the results, higher recognition accuracy than the traditional machine learning algorithms is demonstrated. It can achieve high accuracy in identifying eight underwater acoustic signal modulation methods on the South China Sea dataset with a recognition accuracy of 99.3%.
Rapid warming of sea surface temperature along the Kuroshio and the China coast in the East China Sea during the 20th century

It has been reported that the sea surface temperature (SST) trend of the East China Sea during the 20th century was a couple of times larger than the global mean SST trend. However, the detailed spatial structure of the SST trend in the East China Sea and its mechanism have not been understood. The present study examines the SST trend in the East China Sea from 1901 to 2010 using observational data and a Regional Ocean Modeling System (ROMS) with an eddy-resolving horizontal resolution. A comparison among two observational datasets and the model output reveal that enhanced SST warming occurred along the Kuroshio and along the coast of China over the continental shelf. In both regions, the SST trends were the largest in winter. The heat budget analysis using the model output indicates that the upper layer temperature rises in both regions were induced by the trend of ocean advection, which was balanced to the increasing of surface net heat release. In addition, the rapid SST warming along the Kuroshio was induced by the acceleration of the Kuroshio. Sensitivity experiments revealed that this acceleration was likely caused by the negative wind stress curl anomalies over the North Pacific. In contrast, the enhanced SST warming along the China coast resulted from the ocean circulation change over the continental shelf by local atmospheric forcing.
Regional Short-Term Power Load Forecasting Considering User Electricity Consumption and Seasonal Characteristics

The existing load forecasting of distribution area rarely considers user characteristics and seasonal characteristics, so this paper puts forward a grouping load forecasting method based on big data. Firstly, this approach uses K-means clustering algorithm on the collected user data to form grouped loads. The paper classifies different categories of grouped loads according to Samples of power consumption characteristic data of large users. Then, this paper extracts typical load curves for different industries based on their electricity consumption characteristics. Then, this paper introduces quadratic mode decomposition technology to predict typical daily load curves for different user types. Afterwards, the predicted results of each typical curve are multiplied by the total number of users of that type and stacked to obtain the load prediction value of the target area. This paper verifies the feasibility and practical significance of this method by calculating the actual load in a certain area using Python, which shows that the method proposed in this paper has practical significance and value.
A Vector Fitting Enhanced GSTC-FDTD Method for Broadband Analysis of Dispersive Metasurfaces

Over the past ten years, metasurfaces are a research focus in electromagnetics field, they offer possibilities to manipulate electromagnetic waves in unimaginable ways a decade ago. Here an enhanced finite-difference time-domain method with generalized sheet transition conditions is proposed for simulation of dispersive metasurface. Compared to the previous methods, the susceptibility extracting process is optimized, which makes it more accurate in broadband analysis. An example is presented to validate this enhanced method.
Hardware-Assisted Code-Pointer Tagging for Forward-Edge Control-Flow Integrity

Software attacks typically operate by overwriting control data, such as a return address and a function pointer, and hijacking the control flow of a program. To prevent such attacks, a number of control-flow integrity (CFI) solutions have been proposed. Nevertheless, most prior work finds difficulties in serving two ends: performance and security. In particular, protecting forward edges, i.e., indirect calls, remains challenging to solve without trading off one for another. In this work, we propose Code-Pointer Tagging (CPT), a novel dynamic CFI solution combined with cryptographic protection. Our key observation is that a pointer's message authentication code (MAC) can be associated with the pointer's CFI label used for CFI checks. We find that such an approach not only enables a space-efficient control-flow graph (CFG) storage but also achieves highly-efficient CFI checks performed along with implicit pointer authentication. To enable CPT, we implement lightweight compiler and hardware support. We prototype our design in an FPGA-accelerated RISC-V hardware simulation platform and conduct full-system-level evaluations. Our results show that CPT incurs a 1.2% average slowdown on the SPEC CPU C/C++ benchmarks while providing effective layered hardening on forward-edge CFI.
Performance Comparison of Recurrent Neural Networks for Metamorphic Malware

Metamorphic malware remains a significant challenge in the field of information security as it constantly changes its code and structure, utilizing sophisticated methods to evade detection by antivirus software. This necessitates advanced techniques such as machine learning and deep learning algorithms to effectively detect it. However, the major hurdles lie in the lack of a comprehensive dataset for training algorithms and the continuous evolution of metamorphic malware. To bridge this research gap, a deep learning-based method is proposed that employs bidirectional LSTM (BiLSTM) to detect metamorphic malware based on attack vectors. A comparative analysis is conducted of BiLSTM's performance against other recurrent neural networks (RNNs) such as long short-term memory (LSTM), gated recurrent units (GRU), and a hybrid model of BiLSTM and GRU. The models were trained using a dataset comprising eight categories of metamorphic malware. The results demonstrate that BiLSTM surpasses other RNNs, achieving the highest accuracy of 93.25% compared to 93.20% for LSTM, 91.90% for GRU, and 86.41% for the hybrid model. The use of the outer activation function, Linear, and the inner activation function, SoftSign, in conjunction with the Adam optimizer, significantly contributed to this achievement. The proposed approach has the potential to greatly enhance current malware detection techniques and improve the overall security of computer systems. Furthermore, it can be extended to address other types of malware and cyber threats. By overcoming the challenges of limited training datasets and the ever-evolving nature of metamorphic malware, this research offers practical advantages, including enhanced threat detection capabilities, reduced false positives, and increased system security.
Wide-Beam Millimeter-Wave Circularly Polarized Magneto-Electric Dipole Antenna

A wide-beam millimeter-wave circularly polarized (CP) magneto-electric dipole antenna is proposed in this paper. The antenna inherits the advantages of simple structure and low cross polarization from the magneto-electric dipole antenna, mainly consisting of the magneto-electric dipole, metallic via, and split ring. The half-power beamwidth of the antenna is greatly enhanced by adding metallic vias around the magneto-electric dipole formed by two L-shaped patches. The antenna has a wide half-power beamwidth and a wide 3-dB axial ratio (AR) beamwidth, which is suitable for applications requiring a wide angle range for communication scenarios.
Hospital Data Security using Blockchain and AI

Data is spread vastly over the web. It is a collection of facts and statistic together used for data analysis, mining etc. Data is additionally used as input for numerous computing algorithms to mine out necessary features. Since information is spread all over the web, it rises a lot of trust issues between various organizations. This questions the security of the data on the web and it’s hard to create a cyberspace for this huge amount of data. Providing data security is extremely important in day to day life because there exists a lot of sensitive information, in case it falls into the wrong hands it can cause a lot of problems. Data security is of utmost importance in almost every field like army, hospitals, government offices, colleges, schools etc. In simple words, the process of protecting data from unauthorized access and data corruption is termed as data security. In order to achieve this security, key management, data encryption, hashing and tokenization techniques can be used. Data plays a huge role in any business so it is important to protect your data with the increase in rate of data breach happening. Data security follows the CIA principle. Confidentiality only provides access to authorized users. This can be achieved by implementing access control lists, encryption, strong passwords and two factor authentication. Integrity implies the protection of data from any sort of unauthorized modifications that can disrupt the original data. Digital security can be used to implement integrity. Availability completely focuses on ensuring that all the software and security controls work properly without any interruption. Since the blockchain is a secure chain of individual blocks of transactions there cannot be a chance of hacking or modification of the blocks after it is registered once. Blockchain also provides decentralized identity and other privacy mechanisms. Whereas artificial intelligence helps in enhancing the user security, providing better analytical models and enriching data sets. In order to enhance data security, SecNet is used. It creates a more secured cyberspace.
Performance Impact of Parallel Access of Time Series in the Context of Relational, NoSQL and NewSQL Database Management Systems

Time series data is generated in various application areas, such as IoT devices or sensors in vehicles. This type of data is often characterized by a high resource demand due to the interval at which information is measured ranges from daily down to milliseconds. Next to the frequency, the number of data sources, for example hundreds of sensors in modern airplanes generating time series concurrently, is typical for such big data scenarios. Such scenarios require the persistence of the measurements for further evaluations. In this work, we introduce an artificial data benchmark for relational, NoSQL, and NewSQL database management systems in the context of time series. We compare these databases by having multiple read and write data sources accessing the database management systems simultaneously. The evaluation shows that no tested system outperforms all other systems. While DolphinDB shows the highest read performance in single-user scenarios, CrateDB is able to show its advantages regarding when multiple users access the data simultaneously.
Comparison of Deep Learning Classifiers for the Sudden-Death Positions in Connect6

Board games can be used as a tool for training logic, which has a positive effect on the science popularization education. Connect6 is a very popular puzzle game because it is a fair and highly complex game with simple rules. The objective of this study is to investigate the essential technical challenges involved in constructing a game-based logical learning system for the Connect6 program using deep neural networks, and this study focuses on the comparison of classic deep learning classifiers for the sudden-death positions in Connect6. Our contributions include the followings: 1) Proposing Connect6Net, a deep learning model designed to determine sudden-death positions in Connect6. 2) Experimental results demonstrate superior performance of the Connect6 model with Dense Block technology, compared to its counterpart using Res Block technology. 3) In scenarios with fewer parameters, the model employing SqNxt block technology (C6N-D-s) outperforms its three counterparts.
vimiLab – Teaching Medical Imaging and Radiological Anatomy in a Virtual Reality Environment

Medical imaging is a keystone in medical diagnostics. A novel educational virtual reality application is presented which teaches basic medical imaging techniques in combination with gross anatomy in a safe, immersive and highly interactive way. We investigated the usability of the application in an exploratory study. The results show good overall usability and suggest that the application is easy to learn, efficient to use and provides an attractive learning environment.
Methods for Estimating SOC of LiBs in Electric Vehicles: A Review

The majority of electric vehicles, including cars, buses, motorcycles, and bicycles, use lithium-ion batteries. Every battery-powered device has to carry out the crucial task of estimating the battery's state of charge. One of the most vital battery states to monitor in order to enhance performance and lengthen battery life is the $\text{SoC}$. In terms of electric vehicles (EVs), precise SOC calculation of lithium-ion batteries helps to guarantee battery safety and increase driving efficiency. $\text{SoC}$ cannot be directly measured by the battery management system. The two main SOC estimation types (direct and indirect approaches) are proposed in this review paper, along with other subcategories that list current $\text{SoC}$ estimation trends. The main $\text{SoC}$ estimate proposals that have come up in the literature in past few years are reviewed in this work. The designer can choose the most practical strategy with the aid of this review. This paper sets out the most widely used techniques for estimating the SOC of LiBs, along with the benefits and drawbacks of each technique. The future development direction of SOC estimation is then suggested on the grounds of evaluation of SOC estimation methodologies.
Hand Gesture Classification using Deep learning and CWT images based on multi-channel surface EMG signals

Nowadays, electromyographic signals are one of the most widely used techniques to acquire the electrical activity produced by muscle contractions and relaxations, these signals can be measured in a non-invasive way using surface electrodes devices such as the 8-channel MYO Armband. This paper presents a method for the classification of EMG signals using a superimposed segmentation of the EMG signal of 6 hand gestures acquired from 10 patients with different levels of hand amputation, to subsequently apply the continuous Wavelet transform generating scalogram images forming several datasets with different resolutions suitable for the training of the proposed convolutional neural network. In this experiment, we achieved an average signal classification accuracy of 85.70% and 94.49% in the all-to-one and one-to-one methodologies, respectively. The results generate a model with low computational cost, which can be the basis for actual implementation in a device to classify hand gestures.
Marked in life and death: identifying biological markers of social differentiation in late prehistoric Portugal

Approved: ____________________________________ Thesis Supervisor ____________________________________ Title and Department ____________________________________ Date ____________________________________ Thesis Supervisor ____________________________________ Title and Department ____________________________________ Date 1 MARKED IN LIFE AND DEATH: IDENTIFYING BIOLOGICAL MARKERS OF SOCIAL DIFFERENTIATION IN LATE PREHISTORIC PORTUGAL by Anna Joy Waterman A thesis submitted in partial fulfillment of the requirements for the Doctor of Philosophy degree in Anthropology in the Graduate College of The University of Iowa
Static and Mobile Relay Selection with Chunk-Based Subcarrier Allocation in Uplink OFDMA Networks

Static and mobile relaying is often used to improve the user data rates in wireless communication networks, which would otherwise suffer due to large distances, interference, obstacles, and fading. In this paper, we propose two algorithms for static and mobile relay selection in order to minimize the total distance between a user and the base station. The most efficient algorithm (according to simulation results) relies on a KD-tree, which is a data structure used for fast retrieval of close points. We also propose a subcarrier allocation algorithm which divides the uplink channel subcarriers into a static chunk used for static relays, and dynamic chunk used for mobile relays and direct users. Dynamic allocation should be performed in real-time by the edge network, while static allocation can be perfomed periodically by the cloud network. Simulation results show that a greedy variation of the proposed allocation strategy is faster and more appropriate for the dynamic chunk, while the AP (assignment problem) variation gives slighty higher data rates and can be used for the static chunk.
A E-Band Receiver With 8.8dB Noise Figure in 55-nm CMOS

This paper introduces an E-band receiver with an noise figure(NF) of 8.8dB. The low-noise amplifier using a multitransformer matching network reduces NF and increases the bias voltage of the mixer provided by the transimpedance amplifier(TIA) in the later stages and increases the mixing gain. The 63GHz-71GHz wideband receiver is realized under the 55nm CMOS process. The receiver has a conversion gain of 26.8dB, an NF of 8.8dB, and a 1dB compression point (P1dB) of −24dBm.
Medical Image Classification using CMT and FTGD Layer

Medical image classification is a critical task in the field of healthcare, as it enables the accurate diagnosis and treatment of several diseases. Here, we propose a novel approach for medical image classification by incorporating a Fully Trainable Gaussian Derivative Convolutional (FTGD) layer, on Convolutional Neural Networks Meets Vision Transformer (CMT) model. Our results illustrates the usefulness of the proposed approach for medical image classification and highlight the potential of combining CNNs with the ViT architecture and FTGD layer for improved performance in healthcare applications. We evaluate our proposed approach on a COVID-19 dataset and achieve an accuracy of 85%, outperforming existing methods and also reduces the number of parameters. The proposed method has the potential to assist healthcare professionals in accurately diagnosing and treating various diseases, including COVID-19.