Miracle-dz/UniFAR-FTUs huggingface dataset:
Liquid Crystal-Based Dual-band Reconfigurable Antenna with Parasitic Elements

In this paper, a frequency reconfigurable antenna based on liquid crystal materials is designed by combining parasitic elements and multi-layer antenna technology. Experimental results show that the bandwidth of high frequency band is expanded by 1.3 GHz by adding parasitic elements, the relative expansion reaches 130%, and the reflection coefficient of low frequency band is also effectively suppressed. In addition, the antenna can achieve continuous frequency reconfiguration in the range of 27.47-32.71 GHz and 39.5-41.7 GHz, and the radiation performance is basically stable in the reconfiguration process. It can be widely used in wireless communication and detection fields.
An Unknown Protocol Classification Method based on Clustering Algorithm and Hyperparametric Optimization

In recent years, with the rapid development of Internet communication and satellite communication, and other fields, the number of network protocol types has gradually increased, which contains a large number of private protocols or unknown protocols, which makes the network management and maintenance work increasingly heavy and very difficult. To address this problem, we proposed a classification method of unknown protocols. By preprocessing, feature extraction, and clustering method based on hyperparametric optimization scheme, two classes of classifiers are constructed to classify and identify the unknown protocol data to meet the actual business requirements. The experimental results show that the improved optimization method has a 4.6% increase in the mean probability of obtaining the global optimal solution compared with the traditional optimization method, and the proposed method can effectively identify and classify the protocols.
A recursive algorithm for solving SUR models

The main computational tool for solving SUR or simultaneous equations models is the generalized QR decomposition (GQRD) of an exogenous matrix A and the Cholesky factorization of a dispersion matrix C. Initially the GQRD computes the QRD of A and then the RQD of QC, where Q is an orthogonal matrix. Sequential and parallel strategies have been proposed for computing the GQRD by exploiting the block-diagonal and Kronecker structures of A and C, respectively. The RQD of QC is the most expensive of these operations. An algorithm is presented here that avoids the explicit computation of the RQD of QC. It is based on the strategies for solving augmented systems and updated SUR models. A modification to handle SUR models with unequal observation sizes is discussed.
Multi-time Scale Load Forecasting Based on Wavelet Threshold Denoising and Prophet

Using historical data to predict future energy demand of power system plays a key role in solving the challenge of supply and demand balance of power system brought by renewable energy. In this paper, a multi-time scale power load forecasting method based on wavelet threshold denoising and Prophet is proposed. Firstly, the wavelet threshold denoising algorithm is used to de-noise the historical load data to reduce the influence of inherent noise caused by acquisition equipment and transmission equipment on the results. Then the Prophet algorithm is used to build a time series model of historical data, so as to predict the future power load. This method can predict the power load at different time scales according to different demands. Simulation results show that the proposed method has high prediction accuracy and stable prediction results for different time scales.
Behavior Exploration of Humanoid Robot NAO and Comparative Interaction Study of Autistic Children with the Robot and Human

There lies a good possibility that autistic children of moderately impaired intelligence will find humanoid robots more relatable to them than human being. Hence, they may find it more enticing and comfortable to interact with those humanoid robots. The main goal of this paper is to study these children's interaction with the NAO robot to infer if there is a significant distinction in the communicative behaviors of these children compared to interacting with typical human being. Our analysis of behavioral changes of autistic children with robot and human can be helpful to observe the deficiencies of their communication skill while interacting with typical human being and to see if there is any improvement in their interaction when the human agent is replaced by a robot agent. Depending on the outcome, we can examine the correctness of the probability that a humanoid robot can serve as a better teaching and recreational agent than human.
Scalable Architectures to Support Sustainable Advanced Information Technologies

Advanced computing resources are requested by different interest groups, with the acceleration of digital transformation generated both by the pandemic and by the explosive increase in available information. Data centers have increased significantly over the past years to answer this demand but are now facing energy efficiency issues and performance requirements. Renewable energy is a promising solution by supplying local energy, but this power generation is reduced, intermittent, and requires changing how computation and data storage is managed. Edge/Fog computing is a promising solution to latency concerns proximity to data centers; however, this solution often extends existing clouds, imposing no specific principles to reduce energy and greenhouse emissions. This paper discusses challenges of a scalable architecture to support sustainable advanced information technology requirements inspired by the circular economy characteristics. The architecture has to keep rethinking computation, optimizing deployment, and regenerating energy.
Optimizing PhiNet architectures for the detection of urban sounds on low-end devices

Sound Event Detection (SED) pipelines identify and classify relevant events in audio streams. With typical applications in the smart city domain (e.g., crowd counting, alarm triggering), SED is an asset for municipalities and law enforcement agencies. Given the large size of the areas to be monitored and the amount of data generated by the IoT sensors, large models running on centralised servers are not suitable for real-time applications. Conversely, performing SED directly on pervasive embedded devices is very attractive in terms of energy consumption, bandwidth requirements and privacy preservation. In a previous manuscript, we proposed scalable backbones from the PhiNets architectures' family for real-time sound event detection on microcontrollers. In this paper, we extend our analysis investigating how PhiNets' scaling parameters affect the model performance in the SED task while searching for the best configuration given the computational constraints. Experimental analysis on UrbanSound8K shows that while only the total number of parameters matters when training the model from scratch (i.e., it is independent of the scaling parameter configuration), knowledge distillation is more effective with specific scaling configurations.
Comparison of Non-Communication based DC Load Shedding Schemes

Whenever the total power that can be provided by the distributed energy resources (DERs) is less than the total power demand of the loads, the DC bus voltages start to fall which could lead to power collapse. This paper investigates and compares the performances of the existing non-communication based (decentralized) load shedding schemes in a direct current (DC) microgrid to protect the integrity of the microgrid under a large disturbance. The simulation is carried out in a Matlab environment with various forms of load and distributed energy resources on an IEEE 37 AC Node converted to DC. The findings show that the conventional load shedding scheme could expose critical loads to substantial and lengthy voltage sags. Voltage sags and over-shedding of load could be resolved using combined load shedding scheme. The adaptive schemes minimise the duration and magnitude of voltage drop by utilizing the rate of change of voltage (ROCOV) to achieve a more reliable assessment of the microgrid operating conditions and determine the appropriate load shedding voltage thresholds and time delays. All the schemes could not achieve an optimal load shedding, this work therefore leads to the need for more advanced load shedding schemes that can shed load optimally for future DC microgrids.
Location-invariant representations for acoustic scene classification

High intra-class variance is one of the significant challenges in solving the problem of acoustic scene classification. This work identifies the recording location (or city) of an audio sample as a source of intra-class variation. We overcome this variation by utilising multi-view learning, where each recording location is considered as a view. Canonical correlation analysis (CCA) based multi-view algorithms learn a subspace where samples from the same class are brought together, and samples from different classes are moved apart, irrespective of the views. By considering cities as views, and by using several variants of CCA algorithms, we show that intra-class variation can be reduced, and location-invariant representations can be learnt. The proposed method demonstrates an improvement of more than 8% on the DCASE 2018 and 2019 datasets, when compared to not using the view information.
Analysis of the use of Information System for Accumulating Sex-Disaggregated Data with Analytics of the Barangay Constituents using the Technology Acceptance Model

The study examines how a barangay constituent's sex-disaggregated data is collected via an information system, using the user acceptance model and analytics. The IS was created to assist barangay secretaries in monitoring the volume of households and residents in barangay localities, which helps to reduce the time spent manually recording and monitoring when utilizing the system. The utilization and acceptance of this technology were evaluated by the intended users (N=100). This paper appeals to a varied range of user-related literature that includes a technology acceptance model to illustrate the impact of perceived usefulness and perceived ease of use combined with the external variables of social cognitive theory. The results of a study conducted among experts and users demonstrate the wide acceptance of information systems using external elements like self-efficacy and self-anxiety, which are essential for forecasting and interpreting the research implications made clear.
System Modeling and Design of a Hybrid Renewable Energy System for a Cable Network Head-End Station in Rural Area

This paper focuses on system modeling of a small scale renewable hybrid energy system for a cable network head-end station in rural areas located in central European lower mountain and lowland regions. Based on one year measured energy demand and local weather data the entire system model allows a location dependent energetic simulation and optimization for individual configurations of the photovoltaic, wind energy and battery storage systems. Using selected examples, different system configurations at varying locations are simulated and compared to each other with regard to the number of photovoltaic modules, size of the battery and power of the small wind turbine.
Unsupervised Feature Recommendation using Representation Learning

Today's world extensively depends on analytics of high dimensional sensor time-series, and, extracting informative representation. Sensor time-series across various applications such as healthcare and human wellness, machine maintenance etc., are generally unlabelled, and, getting the annotations is costly and time-consuming. Here, we propose an unsupervised feature selection method exploiting representation learning with a choice of best clustering and recommended distance measure. Proposed method reduces the feature space, to a compressed latent representation, known as Auto-encoded Compact Sequence of features, by retaining the most informative parts. It further selects a set of discriminative features, by computing the sim-ilarity / dissimilarity among the features in latent space using the recommended best distance measure. We have experimented using diverse time-series from UCR Time Series Classification archive, and observed, proposed method consistently outperforms state-of-the-art feature selection approaches.
A Pilot Investigation on the Performance of Auditory Stimuli based on EEG Signals Classification for BCI Applications

Brain Computer Interface (BCI) is a communication pathway between the external devices and the brain signals that doesn’t require any physical activity of the muscular system. Such systems are the only mode of communication for people affected by a number of motor disabilities. In some medical conditions, the person is conscious and awake, but all of his voluntary muscles are paralyzed. Some patients retain vertical eye movement and partially recover from the muscular paralysis. For such patients, there are numerous communication devices available in the market. But for patients who are affected completely, that means the eyes, as well as the muscular activity, is completely paralyzed, hearing is the only mode for them to communicate. In this paper, various auditory stimuli are explored that can be used in BCI applications. In order to create an auditory simulation, comforting sounds to the users such as music and natural sounds are used. This work uses 6 different sounds as auditory stimuli and the brain signals are recorded using an electroencephalogram. The auditory signals are further classified with various classification algorithms such as multi-layer perceptron, random forest, and decision trees. The performance has been analyzed in terms of accuracy, precision, and recall. The average accuracy of 91.56% has been obtained for the random forest, 86.78% for decision trees and 89.92% for multi-layer perceptron. Random forest shows the best classification accuracy when compared to other two classifiers while classifying auditory stimuli-based EEG signals.
Stable Robust Regression under Sparse Outlier and Gaussian Noise

We propose an efficient regression method which is highly robust against outliers and stable even in the severely noisy situations. The robustness here comes from the adoption of the minimax concave loss, while the stability comes from separate treatments of the outlier and noise by an introduction of an auxiliary vector modeling the Gaussian noise. We present a necessary and sufficient condition for convexity of the smooth part of the entire cost under a certain assumption, where a general model is used with its potential use for other applications envisioned. We show that the proposed formulation can be solved via reformulation by the forward-backward-based primal-dual method under the convexity condition. The numerical examples show the remarkable robustness of the proposed estimator under highly noisy situations.
MIDAS: Mutual Information Driven Approximate Synthesis

Applications ranging from the Internet of Things (IoT) to high-performance computing demand energy-efficient hardware for processing and storage. Reducing computation accuracy has shown the potential to achieve high energy efficiency in hardware implementations. In recent years, several automatic approximate logic synthesis techniques have been proposed to build an approximate circuit systematically, trading off accuracy for hardware cost. In this paper, we propose a novel approximate logic synthesis technique to simplify circuits using mutual information by considering the input distribution. Our experimental result shows that our proposed methodology demonstrates improvements in terms of area, delay, and error compared to the state-of-the-art.
A Decision Level Identity Fusion Based Prediction Model for Smooth Fluctuation Products

Nowadays, various indicators with different features are used to analyze the price trend and forecast the future prices for investment decisions. However, for investment products that do not have drastic price trend changes, such as gold, when making decisions on whether to buy or sell based on the predicted price usually does not have good returns. Therefore, the prediction model for investment products with smooth fluctuation (PIPSF) is proposed based on the idea of decision level identity fusion. In this model the 'ideal buy and sell points' is established to decide the buying or selling of investment products. In the case of the large amount of data and feature vectors, the elastic net regression was used for feature selection, it combines the advantages of two regularization methods to ensure both the goodness of fit and the generalization ability of the model. The experimental results show that the model proposed in this paper has higher returns when compared with direct price prediction.
Detection of Tennis Strokes using Wearable Sensor

Analysis has become an important part of pro-fessional sports every major professional team employs expert analysts to improve the team. With the availability of high-quality data, high processing power, and advanced algorithms have been explored. Statistical models made an evolution in sports life. Using wearable sensor to gather data with a combination of an accelerometer and gyroscope of 3 principles axes. That sensor will be worn on wrist of player. Each stroke performed would be saved with its type in the sensor hardware then sent to PC wirelessly for more plays analysis. Using machine learning technique (ANN) on data to analyze for getting detection stroke type and prediction how accurate the player would play in the further plays according to player statistics. The accuracy recorded for this project is 96 percent on classification. For upgrading the strategy-making information predisposing procedures to evaluate data quality. The assessment of the concocted strategy shows promising outcomes contrasted with a comparable technique.
Review on Various Machine Learning Algorithms Implemented in IoT Security

The number of devices connected using Internet of Things (IoT) technology and the data accumulated by these devices, has exploded in the last few years. Because of their resource constraints, participating devices in IoT networks can be difficult, and security on these devices is frequently disregarded. As a result, attackers now have a stronger motivation to object IoT devices. An attack on a network grows rapidly, conventional intrusion detection systems (IDS) struggle to keep up. In this study, we discuss Random Forest, K-Nearest Neighbor, Artificial Neural Network, Decision Tree, Support Vector Machine, and Naive Bayes. The Bot-IoT dataset is used to compare machine learning methods for multi-class and binary classification. We theoretically analyze the said ML algorithms based on their criteria by defining each and every algorithm.
A Survey on Integration of Blockchain and IoT (BIoT): Open Issues, Challenges & Solution

Blockchain and IoT are becoming the buzzwords in the tech world. Blockchain in itself is a very vast topic with various applications and uses but it has its fair share of challenges. Since IoT consists of a vast number of devices, from batterypowered devices with less computational resources to powerful machines, connected over a network it also has hurdles with various barriers. But this paper provides an in-depth review of the current situation of integration of Blockchain and the Internet of Things(IoT) and the Security Issues or Challenges that arise. It starts by discussing the ins and outs of blockchain technology and IoT and their applications. It compares the challenges that arise from using different public blockchain technologies like scalability, interoperability, computational resource constraints, storage, etc. This review also analyzes the current solutions that are present to mitigate the above-mentioned challenges in the integration of Blockchain and IoT. This paper also shines some light on the applications of BIoT and how it is changing and influencing the security challenges in current IoT devices. A detailed and in-depth analysis of this integration of blockchain and IoT was missing so this paper tries to summarize the challenges and solutions.
Transfer Learning for Violinist Identification

Music performer identification is important for music recommendation, music expression analysis and playlist generation. In previous research, audio feature learning methods were commonly used for both singer identification (SID) and instrument player identification (IPID) with good results. In the current deep learning era, SID results are greatly improved using neural networks, however, instrument player identification is rarely investigated in recent works primarily due to the shortage of open-access datasets. To solve this problem, we construct a concerto violin dataset as well as a solo dataset, and present a transfer learning approach for violinist identification from pre-trained music auto-tagging neural networks and singer identification models. We then transfer pre-trained weights and fine-tune the models using violin datasets and finally obtain violinist identification results. We compare our system with a number of state-of-the-art methods and show that our model outperforms them using both of our datasets.
A Multi-Scale Context Aggregation Enriched MLP-Mixer Model for Oral Cancer Screening from Oral Sub-epithelial Connective Tissues

This treatise proposes a dilated convolutional multi-layer perceptron (MLP)-mixer model (hereafter referred to as DiCoMLP-Mixer) for oral connective tissue (OCT) grading. The proposed DiCoMLP-Mixer framework comprehends dense multi-scale contextual feature representation by enabling exponential receptive field expansion without sacrificing resolution via dilated convolutions. The MLP-mixer backbone in the DiCoMLP-Mixer architecture leverages the attention mechanism of a transformer model for saliency abstraction in oral mucosa histopathological images (OMHIs) by the spatial encoding of OMHI patches. This study focuses on the oral mucosa's sub-epithelium region, which has considerable clinical significance but is understudied in the literature. DiCoMLP-Mixer's exhaustive experimental validation on two OCT layers of the sub-epithelium, namely papillary $(L_{1})$ and reticular $(L_{2})$ for oral cancer (OC) grading, classifying the three major oral potentially malignant disorders (OPMDs) and OC from healthy OCTs. The ablation study with the existing MLP-mixer model evinces enhanced OC screening performance, while Grad-CAM heatmaps exhibit DiCoMLP-Mixer's consistent clinical saliency for precise OC detection.
Fast Heterogeneous Task Mapping for Reducing Edge DNN Latency

To meet DNN inference latency constraints on resource-constrained edge devices, we employ heterogeneous computing, utilizing multiple processing elements (e.g. CPU + GPU) accelerate inference. This leads to the challenge of efficiently mapping DNN operations to heterogeneous processing elements. For this task, we introduce a novel genetic algorithm (GA) optimizer. Through intelligent initialization and a customized mutation operation, we are able to evaluate 20x fewer generations while finding superior configurations compared with a baseline GA. Using our mapping optimizer, we find device placement configurations that achieve 15%, 24%, and 31% inference speed-up for BERT, SqueezeBERT, and InceptionV3,respectively.