Miracle-dz/UniFAR-FTUs huggingface dataset:
Sonar Image Target Detection Based on Deep Learning

The most popular and crucial piece of equipment for underwater target detection right now is side-scan sonar, which primarily plays a crucial role in underwater operations. Unfortunately, in general, side-scan sonar images have low resolution and suffer from significant noise interference, making target recognition with these images highly challenging. In this paper, we employ deep learning techniques to recognize underwater targets using side scan sonar imagery. This paper begins by outlining the current state of underwater target detection research as well as briefly describing its importance and historical context. The imaging process of side-scan sonar pictures is studied in greater depth, and it is determined that noise on sonar images is caused principally by the combined effect of external and internal noise. This causes extra noise in sonar pictures, lowering image quality and affecting target identification performance. Proper sonar image pre-processing findings can provide more consistent target detection help. After then, the YOLOv5 target detection algorithm is investigated and improved. By performing several sets of comparison trials, the updated technique improves the accuracy and speed of side-scan sonar image target recognition and finally overcomes the issues of missed detection, false detection, and low accuracy of overlapping tiny object recognition.
The Effect of Out-of-Home Promotion Media on Brand Awareness Impact on Kopi Tubruk Gadjah's Purchase Intention

The factors influencing consumer behavior after seeing advertisements displayed in out-of-home promotion media impact consumer purchasing decisions. This study examines how the characteristics of out-of-home promotion media on brand awareness impact consumer buying intentions for Kopi Tubruk Gadjah products in the city of Bandung. Using purposive sampling, samples were taken from 249 respondents in the city and district of Bandung. Analyzed using Structural Equation Modeling (SEM) and tested empirically using Partial Least Square (PLS). By focusing on the factors of outdoor media promotion and consumer brand awareness, this research will encourage marketers and Kopi Tubruk Gadjah companies to gain consumer interest in making purchases in the future. It was found that the factors of outdoor media promotion (informativeness, credibility, entertainment, and irritation) affect consumer brand awareness. Consumer purchase intention is influenced by consumer brand awareness.
Factors Affecting Structural Leadership of Young Leaders in Thailand

The researcher investigated factors impacting structural leadership in terms of the pressure on young leaders in Thailand. The young leaders studying in a leadership course at a large private university located in Pathum Thani province where the population of this research is. The 356 valid samples were gathered using the survey method. Most of the respondents were females around 20 years old. They were sophomore students at the university. They were from the School of Business Administration and the School of Accounting. Descriptive statistics and multiple regression analysis were utilized. The researcher found that entrepreneurial leadership, interpersonal relationships in terms of accessibility, managerial competency, and transformational leadership had a positive impact towards structural leadership in terms of pressure on young leaders in Thailand respectively. The result of this study could be useful for university business faculties, business organizations, and future research in the management field. This research extended structural leadership with entrepreneurial leadership, interpersonal relationships in terms of accessibility, managerial competency, and transformational leadership.
CP-SLAM: Real-Time Visual SLAM Based on Continuous Probability Map

Robustness is an important criterion for evaluating semantic simultaneous localization and mapping (semantic SLAM). Limited to the computational cost and the complexity of the network, semantic information often has high uncertainty, this paper proposes a new method based on a continuous probability map, which integrates traditional feature points, semantic information, and historical data association. To be specific, an additional tread was added to the SLAM system. This thread utilizes an object detection network to generate high-level semantic information. On top of this semantic information, object detection was transformed into a novel probability map based on the Gaussian distribution assumption. This probability map also makes the optimizer focus on semantic-related objects while maintaining the background features. Further, we designed a continuous mechanism to update the probability map continuously. The mechanism was then applied to nonlinear optimization. The idea of probability map can be regarded as an individual module embedded into any other SLAM algorithm. The algorithm was tested on the TUM dataset. The results of the experiment show that, on average, our method improves localization accuracy by around 20% in indoor environments. Compared with many efficient SLAM systems including Dyna SLAM, the experimental results show that CP-SLAM achieves the effect of improving accuracy in the expected scenario.
Effect of Risk Perception and Anxiety on Travel Abroad during COVID-19: The Case of Bali

The huge negative impact of the COVID-19 pandemic on the tourism industry around the world has been undeniable. Study regarding intention to behavior abroad among international tourists after COVID-19 is still limited. This research develops a conceptual framework that integrates risk perceptions and anxiety on travel intention of inbound tourists to Indonesia during COVID-19. A quantitative approach was selected and self-administrated questionnaires consisting of seven segments (physiological risk, psychological risk, financial risk, time risk, anxiety, intention to travel, and sociodemographic factors) were distributed to respondents in Bali, Indonesia. 487 valid questionnaires were collected from international tourists during their holiday in Bali. The data were analyzed by partial least squares–structural equation modeling (PLS-SEM). Most of the hypotheses were accepted. Furthermore, no significant effects of psychological and physiological risk on tourists’ intention to travel abroad were found. Several practical and theoretical implications for DMOs and marketers are discussed based on the findings.
Distilled Neural Network for Clothes Type Identification

Diverse applications such as criminal forensics and retail customer market segmentation would benefit from the ability to track individuals across multiple cameras. Clothing information is one of the keys to this capability when other identifying information such as the face cannot be reliably extracted. To quickly search for a criminal suspect or re-identify individuals across time and space, filtering by clothing over a given set of cameras for a given time range can dramatically narrow the search. To achieve this capability, we propose a training pipeline that adapts a knowledge distillation neural network to develop a lightweight but effective deep learning model for classifying clothing. The proposed training pipeline produces a small model that achieves 78.70% clothes type classification accuracy on the DeepFashion2 benchmark dataset. An experimental inference time of 0.019 per image on a Nvidia RTX 2070 Super GPU demonstrates that the system has potential for deployment over large-scale camera sensor networks. We expect the new approach to accelerate criminal investigations and enable other analytics requiring reidentification of individuals across multiple cameras at scale.
Boosting the Effect of Digital Mastery on Sustainability Management by Applying Prosocial Leadership

Continuous and disruptive changes require SMEs owners and/or managers to consider long-term business sustainability. This article is interested in elaborating on the sustainability management of SMEs from the perspective of socio-technical theory. A conceptual framework composed of seven hypotheses is developed for examining statistically the effect of digital mastery as a technical aspect and prosocial leadership as a social aspect of sustainability management. An empirical study developed to support this article has invited 297 owners and/or managers of SMEs to participate as respondents. They were members of the Jakpreneur Association, a community of entrepreneurs in Jakarta, Indonesia. Sampling was carried out by a non-probabilistic method with a purposeful approach. The obtained data was structured using partial least squares SEM and computed using the Smart PLS application in version 4.0. The analysis results reveal that digital mastery, as a technical aspect, does affect sustainability management. However, prosocial leadership as a social aspect is effective in boosting the effect of digital mastery on sustainability management. The results confirm socio-technical theory in the context of digital transformation in SMEs.
Platooning-as-a-Service in a Multi-Operator ETSI MEC Environment

Multi-access Edge Computing (MEC) is expected to support platooning of vehicles, by running the control logic that computes the acceleration values of the vehicles based on their position and speed. Connectivity between vehicles and the MEC system is likely to be realized through the mobile network. However, platoons will be composed of vehicles that are customers of different co-located mobile operators, hence– in all likelihood– will also be customers of different MEC systems. In this paper, we devise and evaluate an architectural framework that realizes Platooning-as-a-Service (PlaaS) in a multi-operator MEC environment, compliant with the ETSI MEC standard: we describe the entities involved and their interactions, we release an open-source proof-of-concept implementation for the popular Simu5G simulator, and we evaluate the effect of our framework– and especially its latencies– on platoon stability.
Power Harvesting towards Sustainable Energy Technology through Ambient Vibrations and Capacitive Transducers

As part of the fourth industrial revolution, low-power electronic devices require a power harvesting system to provide energy for their operation because energy harvesting technologies are crucial for enabling 90% of wireless sensor networks. While solar and thermal energy harvesting are popular methods, they have limitations such as dependence on sunlight and thermal gradients, which make them unreliable for low-wattage electronic equipment and wireless sensing networks. Power harvesting from ambient vibrations can lead to a sustainable system. Currently, there is a focus on different power harvesting principles such as electromagnetic, piezoelectric, and electrostatic. This research work presents a novel power harvester design that uses three in-plane capacitive transducers - gap-closing, overlap-varying, and pattern-varying - to operate under ambient vibrations as input energy. With low levels of input vibrations, the device moves, leading to changes in capacitance and the harvesting of reliable electrical power. The design analysis includes capacitance, frequency, and electromechanical evaluations to acquire capacitance matrices, natural frequencies for different modes, displacement covered by moving mass, charge distribution, and pressure distribution.
A Novel Poisoning Attack on Few-Shot based Network Intrusion Detection

With the advancement of Machine Learning (ML) algorithms, more organizations started using Machine Learning based Intrusion Detection Systems (ML-IDSs) to mitigate cyberattacks. However, the lack of training datasets is a major challenge when creating those systems. Therefore, using pre-trained models and small amount of labeled network data or few-shots from internal sources are possible solutions to overcome this challenge. However, using pretrained models or external datasets introduces the risk of poisoned machine learning models. This work investigates a novel poisoning attack that creates a diverse mini cluster of attacks and normal instances around an attack instance, then use the instances in that cluster to poison that instance. The poisoned instances are then injected into training data. A trained model is then created by projecting a labeled data from a poisoned source and the few labeled shots from the target organization. An anomaly-based intrusion detection model is utilized to examine the effectiveness of the introduced approach under the proposed poisoning attack. The results have shown that the attack is effective in the context of few-shot IDS learning.
Electron and Phonon Tranports in a 3D FinFET Transistor

The switch from planar MOSFET to 3-D FinFET technology is one of the best choices to enhance electric performance, even though this switch could damage the transistor due to Self-Heating effect (SHE). In this paper, an electro-thermal model is developed by combining the ballistic-diffusive equation or BDE-model and the drift-diffusion (D-D) model to evaluate the electric and thermal aspects of 14-nm Bulk and SOI FinFET devices. By giving comparisons of the IDS-VGS aspects of our presented work with experimental results reported by HP and INTEL laboratories and numerical data given by TCAD simulators, the validity of the electrical part is verified. Thermal results are also improved by giving comparisons of our thermal characteristics with those obtained by TCAD simulators. The results analysis reveals that the temperature rises slowly in the case of Bulk FinFET comparing to SOI FinFET.
A Comprehensive Study on Traffic Sign Detection in ITS

Intelligent transportation system (ITS) is comprised of several component like Commuter information, Traffic management, vehicle info, public safety etc. To ensure the smooth flow of traffic, the recognition and apprehension of traffic signs are fundamental building blocks of the system. In real time scenario this system Driver Personal Assistant (DAP) can assist driver to travel in stress-free environment and which is one of the reason researchers are engrossed for expansion of such type algorithms in this direction. Due to various symbols of traffic sign images in various countries brought up by shifting perspectives, motion blur, lighting, and other parameters, accurate detection is a big challenging. Several techniques comprise of Artificial Intelligence, Machine learning and Deep Learning has been established in real time span for the enhancement of object detection and recognition of traffic sign. The content is potential for researchers’ association and has developed meaningful interest in several studies on optics -based TSDR (detection and Recognition of Traffic Sign). Three major component detection, Recognition, and classification combined in any algorithm make the system useful for deployment in real time. Variety of techniques had been communicated over a period. All-inclusive brief summary for traffic sign detection is presented in this paper. In addition to this exhaustive summary of algorithms, on detection, recognition and classification are investigated and grouped in tabular representation. An algorithm based on CNN has been implemented and shows better accuracy and less percentage loss in detection of GTSRB traffic sign data set.
A Comprehensive Review on Monitoring Sensors for Cardiovascular Disease Prevention and Management

cardiovascular disease is the leading cause of death worldwide, with 17.9 million deaths occurring annually. This disease an illness that affects the heart and blood vessels, and it has various consequences, including coronary artery disease, heart failure, stroke, and hypertension. Indeed, many factors that can contribute to cardiovascular disease, such as high blood pressure, high cholesterol, and smoking. Therefore, real-time monitoring of cardiac-related parameters is necessary in order to collect patient information, detect abnormal problems, and prevent cardiac complications. In this paper, we review the latest studies concerning the principles and classification of various monitoring sensors, and different cardiovascular parameters, including pulse wave, ECG, SCG/BCG, PCG, and ACG…. First, we examine and compare recent cardiovascular disease monitoring techniques, and then we summarize and discuss different mechanisms of pulse signals. Finally, we propose direction based on current research to improve preventing and managing cardiovascular diseases.
Analysis of multipath effects in automotive MIMO-SAR

This paper analyzes the concept of multipath in automotive radar imaging, particularly in MIMO-SAR imaging. In a typical automotive environment, the radar signal may experience multiple reflections, which leads to the presence of ghost targets in the focused image. These targets may trigger undesired maneuvers from an advanced driving assistance system (ADAS), resulting in possible accidents. In this paper, we show how the position and brightness of the ghost targets are inherently related to the radar's physical layout, including the number of transmitting and receiving elements and their positions. Accordingly, we strongly recommend avoiding the usage of any monostatic or bistatic equivalence in simulation software since they will result in entirely erroneous results. We also show how MIMO-SAR, if implemented with the MIMO aperture orthogonal to the SAR aperture, is intrinsically robust to double bounces resulting in the suppression of ghost targets due to this effect. A set of simulations representing typical automotive scenarios support the theoretical analysis.
Too Legal; Didn't Read (TLDR): Summarization of Court Opinions

Access to justice remains one of the fundamental principles of the rule of law. The original US constitution was four pages and a few thousand words long [1]. But with new additions to laws and bills every year, understanding legal texts or navigating through them in itself requires specialized training and skills. Most of the legal processes and arguments rely on precedents from the past and the previous interpretation of laws. Thus, having access to the last case documents is really important and convenient. Unfortunately, these case documents are often very long, and parsing through them is time-consuming. Case summaries are meant to be of help but are written by experienced professionals and are expensive and labor-intensive. In this article we propose (Natural Language Processing) NLP based legal text summarization approach that can help professionals in writing summaries quickly with a minimum effort or create summaries automatically
Pinch Method of a Moroccan Milk Pasteurization Process

This paper discusses the results of ananalysis of the energy consumption of a Moroccan industrial plant located inthe region of Beni-Mellal/Khnefra. The study was conducted using the pinchmethod on a pasteurized milk production process. The pinch method is used byidentifying the hot and cold streams, as well as the minimal requirement of hotand cold utilities. The energy and economic targets are calculated usingsupertargeting, and the maximum recovery network corresponding to an optimal $\Delta \text{Tmin}$ value is identified. In this study, the reduction of hot utility isapproximately 51% for the pasteurized milk production process, while thereduction of cold utility is approximately 7%.
Chaotic Attractors in Quadratic Discrete Tinkerbell System With Non-Commensurate Fractional Variable-Orders: Complexity, Chaos and Entropy*

This manuscript analyzes the dynamical properties of the incommensurate variable fractional discrete Tinkerbell system. Through time evolution of the states, phase portraits, lyapunov exponents and bifurcation diagrams, it is indicated that the two dimensional incommensurate fractional variable-order Tinkerbell map with discrete time exhibits chaotic and periodic behaviour. The approximation entropy is also used to estimate the complexity of the incommensurate variable fractional-order Tinkerbell system with discrete time. Finally, the 0-1 test plots of the presented discrete Tinkerbell map with incommensurate variable fractional-order validate the findings from the largest lyapunov exponents, bifurcation diagrams and approximate entropy. Numerical results are provided out to simulate the effectiveness of the approach described herein.
Change Detection by Deep Learning Models

Remote sensing change detection (RSCD) has been a demanding research area in the past few decades owing to its diverse applications. Change information can be derived from multitemporal remote sensing images using deep Convolutional neural networks (CNN) as the CNN models are good in classification and feature extraction tasks if trained on large data sets. The present study uses fully convolutional neural network models for feature extraction from bitemporal images in two ways. The first is a transfer learning method using the state-of-the-art VGG19 model, and the second, a fully convolutional neural network (FCN) model with 4 layers. Further, the extracted feature maps are classified by support vector machines (SVM) and a fully connected deep architecture for producing change maps. The proposed methods have been tested on two representative datasets and have obtained results that are superior to those of the relevant methods in terms of accuracy.
Moving Down the Stack: Performance Evaluation of Packet Processing Technologies for Stateful Firewalls

Software-based network security solutions using SDN/NFV provide high flexibility and short development cycles, but may impose a bottleneck onto the network due to their lack of ASIC-based hardware packet processing. To overcome this limitation, several frameworks have emerged to enable flexible high speed packet processing in software, e.g., NAPI, XDP, or DPDK, or on programmable data planes in hardware, e.g., P4. Despite aiming for a common goal, the design principles of these technologies diverge, which raises the question of their suitability for critical security-related network functions, such as firewalls. In this work, we implement a stateful firewall, which is capable of tracking TCP state and sequence numbers, for each of the four aforementioned high speed packet processing technologies and make the firewall modules publicly available. We integrate multithreading strategies, where applicable, and discuss the impact of each packet processing technology during the development process. Finally, we evaluate and compare their performance in terms of throughput in two scenarios following the guidelines of RFC3511 in a 100 Gbps testbed.
High Gain Wideband Non-uniform Ku-band Array Antenna

In this work a cavity backed metallic-slot antenna constructed from design of $8\times 8$ subarrays operating in the Ku-band is introduced. The proposed array is designed to have low-sidelobe levels (SLL) in both E-& H-planes using quasi-Taylor amplitude tapering. The wide operating bandwidth in this Ku-band array comes from using parallel feeding network with one to 64 parallel directions utilizing the corporate feed in order to stimulate the aperture coupling slots in the upper layer above the feeding network layer. The proposed array antenna is designed to have 12.6-14.3% bandwidth, and a peak gain of 32.3 dB within the bandwidth. Also, the Ku-band array antenna has a good cross-polarization discrimination (XPD) better than −41 dB.