Miracle-dz/UniFAR-FTUs huggingface dataset:
Convolutional Neural Networks with Generalized Attentional Pooling for Action Recognition

Inspired by the recent advance in attentional pooling techniques in image classification and action recognition tasks, we propose the Generalized Attentional Pooling (GAP) based Convolutional Neural Network (CNN) algorithm for action recognition in still images. The proposed GAP-CNN can be formulated as a new approximation of the second-order/bilinear pooling techniques widely used in fine-grained image classification. Unlike the existing rank-1 approximation, a generalized factoring (with non-linear functions) is introduced to exploit the intrinsic structural information of the sample covariance matrices of convolutional layer outputs. Without requiring preprocessing steps such as object (e.g., human body) bounding boxes detection, the proposed GAP-CNN automatically focuses on the most informative part in still images. With the additional guidance of keypoints of human pose, the proposed GAP-CNN algorithm achieves the state-of-the-art action recognition accuracy on the large-scale MPII still image dataset.
A Fully Automatic Approach for Fisheye Camera Calibration

An automatic calibration procedure for a fisheye camera is presented in this paper by employing a flat panel monitor. The procedure does not require precise camera-monitor alignment, and any manual input of data or commands, making it useful for factory automation for mass production of such cameras. The fully automatic calibration procedure, which requires the generation of various test patterns on the display, and analysis of fisheye images of these patterns, consists of the following steps: (i) estimate the image center of the camera, (ii) identify the line on the monitor which intersects optical axis of the camera perpendicularly, and (iii) along the above line, obtain calibration data needed in de-warping the fisheye image. Experimental results demonstrate that the proposed approach performs satisfactorily in terms of effectiveness and accuracy.
POLITECNICO DI TORINO Repository ISTITUZIONALE

—Early P2P-TV systems have already attracted millions of users, and many new commercial solutions are entering this market. Little information is however available about how these systems work. In this paper we present large scale sets of experiments to compare three of the most successful P2P-TV systems, namely PPLive, SopCast and TVAnts. Our goal is to assess what level of “network awareness” has been embedded in the applications, i.e., what parameters mainly drive the peer selection and data exchange. By using a general framework that can be extended to other systems and metrics, we show that all applications largely base their choices on the peer bandwidth, i.e., they prefer high-bandwidth users, which is rather intuitive. Moreover, TVAnts and PPLive exhibits also a preference to exchange data among peers in the same Autonomous System the peer belongs to. However, no evidence about preference versus peers in the same subnet or that are closer to the considered peer emerges. We believe that next-generation P2P live streaming applications deﬁnitively need to improve the level of network-awareness, so to better localize the trafﬁc in the network and thus increase their network-friendliness as well.
Implementation of Grid System Over Li-Fi Network

Applications like economic forecasting and seismic analysis require tremendous computation power of the order that can only be provided by powerful computers. Developing software for such complex application requires an uninterrupted access to these resources which is not always easy to acquire for students. These high-end computers are expensive and are not affordable for students. A typical computer lab has enough resources for such high level of computing. However, the resources are dispersed over multiple individual systems. Grid-computing enables the coordinated use of resources to solve problems that arise in dynamic, distributed organizations. Connecting these individual machines in a grid architecture is not enough. It should be explicitly made sure that the latency of communication is low apart from other factors. A fiber optic cable is one solution to the problem. However, it comes with its own drawbacks. In this paper, an idea of building a virtual grid over a Li-Fi network and deploying resources by pooling has been suggested. This pool of resources is controlled by an autonomous server that administrates the working of the Grid. This server to keep track of various parameters like storage space and processing power pertaining to the pooled resources and tasks deployed on the system. It makes use of Li-Fi to establish the communication links at a low cost and higher speeds, thus leading to easy deployment and enhanced functionality of existing lab infrastructure.
FORESEE: Model-based Reinforcement Learning using Unscented Transform with application to Tuning of Control Barrier Functions

—In this paper, we introduce a novel online model-based reinforcement learning algorithm that uses Unscented Transform to propagate uncertainty for the prediction of the future reward. Previous approaches either approximate the state distribution at each step of the prediction horizon with a Gaussian, or perform Monte Carlo simulations to estimate the rewards. Our method, depending on the number of sigma points employed, can propagate either mean and covariance with minimal points, or higher-order moments with more points similarly to Monte Carlo. The whole framework is implemented as a computational graph for online training. Furthermore, in order to prevent explosion in the number of sigma points when propagating through a generic state-dependent uncertainty model, we add sigma-point expansion and contraction layers to our graph, which are designed using the principle of moment matching. Finally, we propose gradient descent inspired by Sequential Quadratic Programming to update policy parameters in the presence of state constraints. We demonstrate the proposed method with two applications in simulation. The ﬁrst one designs a stabilizing controller for the cart-pole problem when the dynamics is known with state-dependent uncertainty. The second example, following up on our previous work, tunes the parameters of a control barrier function-based Quadratic Programming controller for a leader-follower problem in the presence of input constraints.
Demonstration of a Millimeter-wave High-Power Transceiver Module using AlN Interposer

This paper presents a GaN-based transceiver module featuring a maximum equivalent isotropically radiated power (EIRP) of 30.3 dBm at 38 GHz. With the adoption of AlN substrate as interposer, an effective management of the heat generated during high power operation mode was achieved. Experimental verification with a 24-hour over-the-air (OTA) test was performed while driving the transceiver at its maximum EIRP. Such verification has demonstrated the great potential of adopting the developed AlN substrate technology as interposer for high-power transceivers at millimeter-wave frequencies.
Traffic Sign Detection Using Template Matching Technique

Advancement in safety features to prevent ignorance of traffic sign boards is a challenge faced by drivers in daily routine. By overlooking traffic road signs, one can lead to road accidents which can be of high-risk for the driver as well as the fellow passengers. Therefore, using Driver Assistance System to assist the driver with mandatory and cautionary road signs can be of important help in prevention of road accidents. The paper explains Template Matching technique of image processing for detecting Traffic signs mounted alongside the road. Here, the object of interest, i.e., traffic sign board, is selected and learned by the system which is then detected where the Region of Interest is in form of green-yellow bounding box. The system is alert about the reappearance of the object, that is, after the departure of the current frame. The output is given both in audio as well as in display message format. This combination of output enables the driver to stay alert while driving the vehicle and prevents any kind of mishap.
Lightweight Secure-Boot Architecture for RISC-V System-on-Chip

Securing thousands of connected, resource-constrained computing devices is a major challenge nowadays. Adding to the challenge, third party service providers need regular access to the system. To ensure the integrity of the system and authenticity of the software vendor, secure boot is supported by several commercial processors. However, the existing solutions are either complex, or have been compromised by determined attackers. In this scenario, open-source secure computing architectures are poised to play an important role for designers and white hat attackers. In this manuscript, we propose a lightweight hardware-based secure boot architecture. The architecture uses efficient implementation of Elliptic Curve Digital Signature Algorithm (ECDSA), Secure Hash Algorithm 3 (SHA3) hashing algorithm and Direct Memory Access (DMA). In addition, the architecture includes Key Management Unit, which incorporates an optimized Physical Unclonable Function (PUF) for providing keys to the security blocks of the System on Chip (SoC), among which, secure boot and remote attestation. We demonstrated the framework on RISC-V based SoC. Detailed analysis of performance and security for the platform is presented.
Chicken Disease Classification from Faeces Samples Using a Fine-Tuned EfficientNet B5 Model

Poultry farming is critical for providing the world's protein requirement, but it feces challenges such as viral infections that influence bird health and production. It is critical to obtain a diagnosis as soon as possible in order to combat the many chicken diseases. This study aims to expand the field of poultry disease diagnostics by using advanced deep learning techniques to diagnose common chicken diseases using fecal samples. The goal of this study is to employ deep learning methods, specifically the EfficientNet B5 model, to classify four common chicken diseases: coccidiosis, salmonella, Newcastle disease, and healthy birds, with a total of 8000 fecal images. In the study, the Adam optimizer was utilized to simulate 15 epochs and 32 batch sizes in order to fine-tune the EfficientNetB5 transfer learning model. The fine-tuned transfer learning model has a 95% overall accuracy. The study's potential benefit rests in its ability to speed disease identification processes, reducing economic losses and enhancing the overall sustainability of poultry production systems.
Performance of Conventional and LR Based Passive Radar Detectors in Ground Traffic Applications

Bistatic radar clutter can be characterized considering the Cross-Ambiguity Function (CAF) output as the observation space, for formulating the optimum Neyman-Pearson detector. In a previous study, a bank of Likelihood Ratio (LR) detectors was designed, but a basic passive radar solution composed of an incoherent Constant False Alarm Rate applied to the CAF intensity was used as reference. In this paper, the study is completed considering a widespread solution based on an Extensive CAncellation filter before CAF generation. Performances are estimated using real data acquired in a semi-urban scenario for the detection of ground targets. Estimated probabilities of detection and false alarm confirm the superior performance of the bank of LR filters compared to the basic and the new reference solutions.
Drug-Disease Association Prediction Based on Neighborhood Information Aggregation in Neural Networks

Computational drug repositioning plays a vital role in the prediction of drug function. Many new functions discovered have been confirmed. In comparison with traditional drug repositioning, computational drug repositioning shortens the time and reduces labor. Thus, it has received wide attention in recent years. However, prediction remains a considerable challenge. In this paper, a method called HNRD is introduced to predict the link between drugs and diseases. It is based on neighborhood information aggregation in neural networks which combines the similarity of diseases and drugs, the associations between the drugs and diseases. Compared with the state-of-the-art method before, our method has achieved better results, with the best AUC of 0.97 in one of the golden datasets. To better evaluate our approach, we also performed data analysis based on one-to-one association’s prediction and robust analysis by testing on different datasets. All the results prove the excellent performance of prediction. Source codes of this paper are available on https://github.com/heibaipei/HNRD.
A Comparative Analysis of Top 5 Fast Food Restaurants Through Text Mining

Text mining is a logical way of mining content and to know about feelings of users. Social media plays a vital role in letting people know about each other views. An ever increasing number of different online integration brands are working on Facebook, Instagram, Twitter and other social networks to furnish several directions and collaborate with multiple consumers. Social media helps different companies to enhance their businesses and get audience feedback for the betterment of their business timeline. Consequently, a lot of customer produced content is uninhibitedly accessible via web-based networking media sites. To increment upper hand and satisfactorily evaluate the focused condition of many companies, they need to work on and analyze the content that will affect their competitors who are also working on social integration networks. This paper represents a comparative analysis of five different social media pages and we have applied text mining to extract data from Facebook or Twitter of five fast food restaurants i.e. KFC, McDonald's, Burger King, Hardees and Howdy.
Compact out-of-Phase Wideband Substrate Integrated Waveguide based Power Divider Loaded by Slots for Ku and K Band Applications

In this paper a novel Substrate Integrated Waveguide (SIW) based single layer ground-loaded compact wideband out-of phase equal power divider is proposed . The wide-band and out-of-phase operation of the proposed power divider is obtained by creating defects in the ground plane with rectangular slots. The Defected Ground Structure (DGS) allows the power divider to exhibit a wide passband. The obtained passband is 11.5 GHz wide considering the return loss better than -10dB. Compactness in the proposed design is attributed to the dispersion characteristic of the slow-wave. The proposed design working in the passband from 14.5 GHz to 26 GHz is fabricated and tested. The size of the proposed design is 0.57 λ2g excluding feed lines. Here λg is the guided wavelength at free space. The measured amplitude imbalance of (0±1) dB is obtained within the passband. The measured and simulated results are compared and found with in good agreement.
Multi-Class Text Classification using Machine Learning & Deep Learning

Text classification indeed holds a central position in the field of natural language processing (NLP) and has a wide range of applications across diverse domains. In this paper, we investigate the effectiveness of BERT & DistilBERT embeddings in combination with long short-term memory (LSTM), convolutional neural networks (CNN), and bi-directional LSTM (bi-LSTM) architectures for text classification on the widely recognized 20 Newsgroups dataset. We propose an approach that utilizes BERT & DistilBERT embeddings to capture contextual information and feed it into the CNN, LSTM, and bi-LSTM models. This combination enables the models to effectively learn hierarchical representations and capture both local and global dependencies in the text data. Experimental results demonstrate that our proposed CNN, LSTM, and bi-LSTM models outperform Siamese networks, which have previously been explored for text classification on the 20 Newsgroups dataset. Our models consistently demonstrate superior accuracy and deliver enhanced performance in terms of precision, recall, and F1-score across diverse classes. Our proposed models not only outperform Siamese networks but also provide insights into the importance of leveraging contextual embeddings for more accurate and robust text classification. A maximum accuracy of 92.94% was achieved when using distil-BERT with bi-LSTM and a minimum of 87.76% when using LSTM.
Automatic tissue segmentation by deep learning: from colorectal polyps in colonoscopy to abdominal organs in CT exam

Automatic tissue segmentation is extremely helpful in medical imaging related work. In this paper, we attempted to train two existing deep neural networks, SegNet and DeepLab, to solve two clinical imaging problems relevant to this topic. One is to locate the colorectal polyps in the colonoscopy images, and the other is to delineate the lung in CT images from axial direction. In order to enhance the segmentation capability of the two networks, the reversed version of long short term memory (LSTM) network are integrated with them by parallel connection. The performance is evaluated by mean intersection-over-union (IOU). We found that introducing LSTM is beneficial to segmentation of polyps, but not that significant for delineating the lung. The relevant results are reported in this work
Tracking Driver’s Foot Movements Using mmWave FMCW Radar

Monitoring driver’s foot movements can be used to assess their preparedness to assume the vehicle’s control and to provide insights into their overall condition. While cameras and capacitive sensors have been employed to gather data on pedal-bound foot actions, the accuracy of readings provided by these systems can be compromised under constrained light conditions and fluctuations in temperature and humidity levels, respectively. Recently, radio-frequency waves were utilized to remotely recognize driver’s activities. However, most of the RF-based works focus on the identification of the driver’s intent by extracting information from the movements done by the driver’s upper body. In this paper, we present a feasibility study on the application of millimeter-wave frequency-modulated continuous-wave radar for tracking a driver’s foot. Movements such as pedal actuation and transitions between pedals are analyzed, and experimental results demonstrate the potential of integrating the proposed method into advanced assistance systems.
Deep Dual-view Network with Smooth Loss for Spinal Metastases Classification

Spinal metastases have a high incidence among cancer patients and may later develop to metastatic spinal cord compression (MSCC). Early detection of spinal metastases is critical for optimal treatment. The diagnosis is usually facilitated with computed tomography (CT) scans, which requires considerable efforts from well-trained radiologist. In this paper, we explore automatic spinal metastases classification based on CT images. Considering the unique characteristics of spinal CT images, a novel Deep Dual-view Network is proposed which contains two branches: a X-Y Conv Branch to extract the features for each individual cross-sectional image slice, and a Z Conv Branch to capture z-direction features from neighboring images. The features from the two branches are then fused to generate the final prediction, which imitates the doctor’s way of combining cross sections with sagittal or coronal sections. Considering a tumor usually presents in multiple consecutive image slices, a smooth loss is introduced to maintain the label consistency of adjacent images. To validate the proposed approach, we collect a dataset of 316 patients with spinal metastases. Experimental results on this data set have demonstrated the effectiveness and the robustness of the proposed Deep Dual-view network.
Design Patterns in Building Software for a Middle Size League Robot Using Robot Operating System

There are many ways to build the software for a Middle Size League (MSL) robot. A common problem in developing software for any long-term project including MSL is enabling future developers to maintain and continue further research on the project. A solution to this problem is to use an already existing software development framework. In recent years, the Robot Operating System (ROS) has become the ’de facto’ standard framework for robotics software development. This paper will explore the advantages found in using ROS as the framework to build a software for a robot The three main advantages that were found in using ROS are data sharing, hardware abstraction, and program concurrency. These advantages allowed the team to build modular software that is more maintainable. Furthermore, there was minimum computation cost found in using ROS. Lastly, the locomotion system was tested. When given a linear movement command, the locomotion system has an average actual distance error of 8.84 cm with a standard deviation of 6.70 cm. It is also found that the average computed error is 3.54 cm with a standard deviation of 1.49 cm.
Pre-processing Techniques for Performing Hotel Review Sentiment Analysis

In recent times, AI researchers have shown an increasing fascination with sentiment analysis, primarily due to its promising potential for profitable commercial uses. The initial stage of text mining in a Sentiment Analysis system involves crucial pre-processing steps. Given the frequent deviations from grammar and spelling norms in textual data, these steps become essential for data refinement. Moreover, prior to commencing the analysis it, pre-processing plays an essential part in standardizing the text. The main target of this investigation is to emphasize the importance of pre-processing techniques and showcase their capacity to enhance system accuracy. The study delves into diverse pre-processing techniques and compares their respective levels of accuracy. This comparative assessment aims to pinpoint the most effective strategies. In addition to a comprehensive evaluation of each method, the study delves into the rationale behind the observed accuracy enhancements. It's widely acknowledged that sentiments are often encapsulated in reviews, as individuals, particularly those who are enthusiastic or socially engaged, tend to articulate their opinions through this medium. Such reviews not only capture the viewpoints of their creators but also encapsulate their emotions. Given the unstructured nature of this textual content, effective handling necessitates the application of pre-processing techniques to prepare the data. Subsequently, the pre-processed data serves as a foundation for feature extraction. Approaches utilized to extract features encompass a range of methods, encompassing Bag of Words (BoW), Term Frequency - Inverse Document Frequency (TF-IDF), word embeddings, and Characteristics acquired through Natural Language Processing (NLP), such as word frequency and noun enumeration. These techniques constitute only a portion of the diverse methods employed.
Defected Ground Structure (DGS)-Based Koch Fractal Antenna for S and C-Band Applications

This paper presents a Koch fractal monopole antenna, which resonates at <tex>$2\text{GHz}$</tex> and <tex>$3.44\text{GHz}$</tex> with <tex>${S_{11}}$</tex> values of <tex>$ - 20.42\text{dB}$</tex> and <tex>$ - 40.83\text{dB}$</tex> respectively. The radiator is miniaturized by using the fractal technique, and wideband is achieved with the help of the Defected ground structure technique. To achieve impedance matching, a microstrip feedline is considered. FR4 substrate having a permittivity of 4.4 is considered for the design. Overall, the dimensions of the proposed design are restricted to <tex>$58{\mathrm{\;mm\;}} \times {{\;}}38{{\;mm\;}} \times {{\;}}1.6{{\;mm}}$</tex>. This radiator covers the frequency range from <tex>$1.76\text{GHz}$</tex> to <tex>$4.29\text{GHz}$</tex> with an impedance bandwidth of <tex>$83.58{{\% }}$</tex>. This monopole exhibits a VSWR of 1.02. In the operational frequency range, the proposed structure maintains a positive gain with a peak gain of 4.06 at 4.19 GHz. Hence, the proposed antenna can be a suitable candidate for <tex>$\mathrm{S}$</tex> and <tex>$\mathrm{C}$</tex> band applications.