Reviewerly/exHarmony huggingface dataset:
Obstacle Avoidance Algorithm for Circular Objects During Multiple Target Waypoints Navigation Mission of a Fixed-Wing UAV Using Polar Coordinates.

This paper presents an obstacle avoidance algorithm that avoids 2D circular objects during an autonomous waypoint navigation flight of a fixed-wing UAV. We named the obstacle avoidance algorithm the Pre-Determined Virtual Circular Obstacle Avoidance Algorithm (PDVCOAA). If a circular obstacle is situated in between two consecutive target waypoints, the PDVCOAA generates an updated trajectory with new target waypoints to avoid the obstacle(s). The new target waypoint navigation plan is uploaded from a ground station to the flight controller on the UAV. The ground station software used in this work is QGroundControl and the flight controller is the Pixhawk 4 Mini, with PX4-Autopilot as its firmware. The performance of the PDVCOAA obstacle avoidance algorithm is assessed using a sample flight plan shared by the organizers of the 2022-23 SUAS competition through Software In The Loop (SITL) testing with a simulated fixed-wing vehicle as well as several custom flight plans in the Gazebo simulator. Once the obstacle avoidance algorithm was validated in the simulation environment, it was flight tested on a custom designed fixed-wing UAV. From the flight tests, we’ve validated that our UAV can avoid obstacles with a radius of 150 ft. and an acceptance target waypoint radius 33.2 ft (default setting in PX4 firmware) and 25 ft (acceptance radius specified in 2022-23 SUAS competition rules).
DWGAN: a dual Wasserstein-autoencoder-based generative adversarial network for image super-resolution

Significant progress has been made with image super resolution using deep learning in recent years. However, existing methods aim at minimizing differences between pixels, which means they fail to consider the distribution difference while reconstructing images. To address this challenge, we propose DWGAN: a dual Wasserstein-Autoencoder based generative adversarial network. Our DWGAN successfully implemented the adversarial training process both in the latent space and the image space. In the latent space, the adversarial training process successfully models the latent distribution with Wasserstein Autoencoders and the adversarial training in the image space achieves better reconstruction performance. Besides, we also utilize tree-structure residual encoders to extract 2-dimensional latent features of the same size as low-resolution (LR) inputs, which enables pixel-wise matching. Experiments show that our DWGAN achieves better visual and quantitive results compared with other GAN-based models.
"You AR' right in front of me": RGBD-based capture and rendering for remote training

Immersive technologies such as virtual reality have enabled novel forms of education and training, where students can learn new skills in simulated environments. But some specialized training procedures, e.g. ESA-certified soldering, still involve real-world physical processes with physical lab equipment. Such training sessions require students to travel to teaching labs and may interrupt everyday commitments for a longer period of time. There is a desire to make such training procedures more accessible remotely while keeping any student-to-teacher interaction natural, personal, and engaging. This paper presents a prototype for a remote teaching use case by rendering 3D photorealistic representations into the Augmented Reality (AR) glasses of a student. The teacher is captured with a modular RGBD capture application integrated into a web-based immersive communication platform. The integration offers multiple real-time capture calibration and rendering configurations. Our modular platform allows for an easy evaluation of different technical constraints as well as easy testing of the use case itself. Such evaluation may include a direct comparison of different 3D point-cloud and mesh rendering techniques. Additionally, the overall system allows immersive interaction between the student and the teacher, including augmented text messages for non-intrusive notifications. Our platform offers an ideal testbed for both technical and user-centered immersive communication studies.
P4SGD: Programmable Switch Enhanced Model-Parallel Training on Generalized Linear Models on Distributed FPGAs

Generalized linear models (GLMs) are a widely utilized family of machine learning models in real-world applications. As data size increases, it is essential to perform efficient distributed training for these models. However, existing systems for distributed training have a high cost for communication and often use large batch sizes to balance computation and communication, which negatively affects convergence. Therefore, we argue for an efficient distributed GLM training system that strives to achieve linear scalability, while keeping batch size reasonably low. As a start, we propose P4SGD, a distributed heterogeneous training system that efficiently trains GLMs through model parallelism between distributed FPGAs and through forward-communication-backward pipeline parallelism within an FPGA. Moreover, we propose a light-weight, latency-centric in-switch aggregation protocol to minimize the latency of the AllReduce operation between distributed FPGAs, powered by a programmable switch. As such, to our knowledge, P4SGD is the first solution that achieves almost linear scalability between distributed accelerators through model parallelism. We implement P4SGD on eight Xilinx U280 FPGAs and a Tofino P4 switch. Our experiments show P4SGD converges up to 6.5X faster than the state-of-the-art GPU counterpart.
Jump-Starting Software Engineering Careers

This article explains how a major Australian university is partnering with the local IT industry to better prepare IT students for the start of their careers in software engineering through real-world industry projects. We present experiences from three different viewpoints.
A new two-stream structure preserving network for X-ray enhancement

Generative adversarial network (GAN) is applied in X-ray enhancement, but the tiny structure of X-ray is hard to be maintained by the generator of the GAN. To solve this problem, a new two-stream GAN (named T-S GAN) is proposed to enhance X-rays and guarantee better structure preserving at the same time. In the proposed generator of T-S GAN, the two-stream feature maps are extracted from original and detail X-rays, and then the two-stream features are fused by a coarse-to-fine strategy. Meanwhile, by introducing detail and structure constraints, a mixed loss function is designed for optimizing the T-S GAN. Lastly, to prove the correctness and effectiveness of T-S GAN, a novel dataset of X-ray with finger and wrist fracture is originally created. There are plenty of hairline fractures in the dataset, which can be used to solve the problem of verifying that the tiny structure is preserved by T-S GAN. Experimental results show that the proposed method, compared with the state-of-the-art methods, has lower mean absolute error (MAE), higer peak signal to noise ratio (PSNR) and structure similarity (SSIM) values. A combination of T-S GAN and the state-of-the-art object detection method can be potentially applied for modern medical detection.
Low-Illumination Image Enhancement for Foreign Object Detection in Confined Spaces

Low-illumination image enhancement (LIIE) in confined spaces is a challenge faced in industrial inspection and diagnostics areas. Most existing methods cannot handle extremely dark images captured in confined spaces. This article reports a residual deep curve estimation network (ResZero) to enhance such dark images for foreign object detection (FOD). To reduce information loss when enhancing extremely dark images, residuals are added to the backbone of the proposed ResZero to pass forward features in the middle layers. To solve noise and color cast problems in the enhancement process, a new loss function is defined by considering the feature-preserving and RGB channel averaging simultaneously. Moreover, the dynamic range of the public datasets has been expanded by adding extremely dark images captured in confined spaces. Experiments with various extremely dark scenes demonstrated that the enhanced image’s quality average score (BRISQUE) of the proposed ResZero is 22.25 better than all other methods(0.22 higher than the second place). In FOD experiments, the proposed method is with the highest precision.
A Super Lightweight and Efficient SAR Image Ship Detector

The realm of Synthetic Aperture Radar (SAR) ship detection has witnessed widespread adoption of deep learning, owing to its exceptional detection accuracy and end-to-end capabilities. Despite these advantages, the current SAR ship target detection methods still face the challenge of detecting small-scale targets and are difficult to be deployed on satellite platforms due to their complex models and huge computational effort. To overcome these problems, based on the YOLOv5 architecture, we present a super lightweight and efficient SAR ship target detection method named SLit-YOLOv5. Our proposed model comprises two essential components, IMNet and Slim-BiFPN. IMNet serves as the backbone feature extraction network, significantly enhancing the feature extraction capability while reducing the number of parameters by half. Slim-BiFPN achieves adaptive fusion of multi-scale features with fewer parameters. To validate the proposed model, we conducted an experimental evaluation on the SAR ship detection dataset (SSDD), and the results show that our SLit-YOLOv5 model outperforms the currently popular lightweight SAR ship target detection methods with high detection accuracy, low floating-point operations, and very few params.
Comparative Analysis of Artificial Intelligence Techniques for Sentiment Analysis and its Challenges

Evolution of Artificial Intelligence proves better solutions in wide variety of applications. Due to advancement of internet and social media, data is increasing exponentially. It is becoming key platform and source of information where peoples express their implicit thoughts, feeling, and emotions. It is need to analyze such data for different application. Sentiment analysis is the application which is used to extract accurate sentiments from user data. It is deals with extracting sentiments, opinion, emotion, aspect, view from the text, videos, emoticons, speech etc. There are different challenges of sentiment analysis. Varieties of techniques are developed to overcome such challenges. Nature inspired algorithms becoming emerging area of a research in AI to get optimize results. Machine Learning and Deep learning techniques playing significant role for problem solving in different areas. Knowledge graph is the emerging approach used for sentiment analysis. This study focuses on comparative analysis of Bioinspired meta-heuristic optimization algorithms, Machine Learning, Deep Learning techniques and Knowledge Graph for sentiment analysis with its challenges.
The Evaluation of Cryptocurrency: Overview, Opportunities, and Future Directions

Money is a fundamental requirement for every individual to meet their various needs. However, despite the growing popularity of cryptocurrency, there is a lack of literature reviews that comprehensively examine this subject. Cryptocurrency is a blockchain-based technology that operates as a decentralized digital currency. With the advancement of modernization and globalization in the Fourth Industrial Revolution, cryptocurrency has become increasingly popular. Despite its lack of physical form, it functions like traditional currencies and has a volatile exchange rate, making it appealing to traders. Transactions in cryptocurrency take place online directly between individuals without the involvement of intermediaries. However, despite its efficiency and convenience, cryptocurrency has its downsides, including the absence of a responsible authority to address transaction-related issues and the prevalence of money laundering activities. These challenges must be addressed to maximize the potential of cryptocurrency and blockchain technology in today’s globalized world.
NSGAII for Travelling Thief Problem with Dropping Rate

Most of the real life optimization problems often involve simpler problems which may be interwoven together. One such problem introduced recently is the travelling thief problem (TTP) which combines the classical Travelling salesman problem and the knapsack problem. This paper addresses one of the variants of TTP namely TTP2 which has not been studied so far. In TTP2 a thief has to visit a number of cities exactly once. Each of these cities have certain items with some profit and weight associated to each of the items. The thief has to pick items from the cities, while satisfying knapsack capacity constraint. Further, with the increase in the weight of knapsack, the velocity of thief reduces while profit associated with an item decreases by a factor (dropping rate) which depends on its duration in the knapsack. The objective of the thief is to minimize total time while total profit of the item is to be maximized. This research study has designed and implemented a population based evolutionary algorithm, NSGAII to tackle TTP2. Further, this research study proposes three construction heuristics and problem specific crossover and mutation operators for NSGAII. Experiments are conducted on a test set containing 20 benchmark instances to test the performance of the metaheuristic even though competitive testing is not done as no previous work for TTP2 is available in the literature.
TASQ: Temporal Adaptive Streaming over QUIC

Traditional Adaptive BitRate (ABR) streaming faces a challenge of providing smooth experience under highly variable network conditions, especially when low latency is required. Effective adaptation techniques exist for deep-buffer scenarios, such as streaming long-form Video-on-Demand content, but remain elusive for short-form or low-latency cases, when even a short segment may be delivered too late and cause a stall. Recently proposed temporal adaptation aims to mitigate this problem by being robust to losing a part of the video segment, essentially dropping the tail of the segment intentionally to avoid the stall. In this paper, we analyze this approach in the context of a recently adopted codec AV1 and find that it does not always provide the promised benefits. We investigate the root causes and find that a combination of codec efficiency and TCP behavior can defeat the benefits of temporal adaptation. We develop a solution based on QUIC, and present the results showing that the benefits of temporal adaptation that still apply to AV1, including reduced stall time up to 65% compared to the original TCP-based approach. In addition, we present a novel way to use the stream management features of QUIC to benefit Quality-of-Experience (QoE) and reduce wasted data in video streaming.
The Impact of Latency on Target Selection in First-Person Shooter Games

While target selection in a 2D space is fairly well-studied, target selection in a 3D space, such as shooting in first-person shooter (FPS) games, is not, nor are the benefits to players for many latency compensation techniques. This paper presents results from a user study that evaluates the impact of latency and latency compensation techniques on 3D target selection via a bespoke FPS shooter. Analysis of the results shows latency degrades player performance (time to select/shoot a target), with subjective opinions on Quality of Experience (QOE) following suit. Individual latency compensation techniques cannot fully overcome the effects of latency but combined techniques can, letting players perform and feel as if there is no network latency. We derive a basic analytic model for the distribution of the player selection times which can be part of a simulation of a full-range of FPS games.
Interactive Dashboard for Education Abroad with Geospatial Information and Predictive Analysis

To advance one’s career in the field of study one chooses, a master’s degree has recently become a prerequisite. The idea of earning a master’s degree has spread widely for purposes other than merely expanding one’s knowledge. Finding the ideal university that would satisfy all of our demands depends on various factors. Everyone wanting to pursue a post-graduate degree would be interested in learning some information that will help evaluate whether they are qualified to enroll in their ideal university and, if not, how to prepare themselves since students are more interested to enroll in Master’s course, finding colleges, their requirements, and much other information have been stressful and laborious. As a result of this experience, this study has developed a comprehensive dashboard that would provide students with all the data they could need to understand how they stack up against other candidates for coveted slots at their selected universities.
A Black-box based Attack Generation Approach to Create the Transferable Patch Attack

Due to the recent advancement of deep learning methodologies, face recognition has seen increased success. However, researchers found that adversarial attacks can be launched against deep learning-based systems. The face recognition systems can be tricked by small changes in an input image. The human visual system is incapable of detecting these changes. In this work, a black-box attack generation method is proposed to create a transferable patch attack (TPA) using the generative adversarial network (GAN) to deceive the current face recognition systems. The generator and discriminator networks are used in the TPA attack to generate the adversarial images. The Labeled Face in the Wild (LFW) dataset is used as input for the proposed TPA attack, which creates the image by adding noise in the form of a patch. These patches are added in such a manner that they are invisible to the naked eye. FaceNet, ArcFace and CosFace face recognition models are used for the attack testing and also the proposed attack is compared to existing attacks. The purpose of this paper is to suggest a black-box attack and increase the transferability of adversarial attacks. In the black-box attack, the attacker doesn’t know any information about the target model. The quantitative findings demonstrate that the proposed attack successfully misclassified the most recent FR models and achieved a higher attack success rate.
Deep Learning Based Model for Fake Review Detection

In present time, peoples are more inclined towards the e-commerce for their purchases and their choices are much influenced by the reviews available over there as review plays an important role in making their decision. If the reviews are more positive the possibility to buy the product is comparatively high. Here, the necessity arrives to develop a sustainable approach for the detection of malicious reviews to save the customers from the fraud. There are many sites or agencies are available which are hired by the merchandise to generate the positive reviews for them to increase their sales or damage the competitor’s product sales. Deep learning methodologies for malicious review detection includes, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) are proposed in this paper. We have also compared the performance of these methods with state of arts techniques such as Naive Bayes (NB), K Nearest Neighbour (KNN) and Support Vector Machine (SVM) for the detection of fake reviews and ultimately, its efficiency is illustrated for both the traditional and the deep learning classifiers.
A Platform for Tracking Charity Donations using Blockchain

Charity organizations are susceptible to the same kinds of fraud that harm businesses because donors lack confidence in how their money is being used. The solution is to provide a unified platform for monitoring donations that will track all data related to donation transactions and donors. Lack of trust on the part of donors in how donations are used is a result of skepticism over how donations are used. Therefore, building a single platform for tracking donations that manage all information regarding gift transactions is essential since charities are vulnerable to fraud that affects corporations, such as executive misuse of funds and embezzlement.
Prediction of Political Biasness of Statements

Sentiment analysis, commonly referred to as opinion mining, is a branch of natural language processing (NLP)that deals with identifying and extracting subjective information from text, such as opinions, attitudes, and emotions. In this study, the main aim to develop a machine learning-based model to predict the political biasness of tweets. Tweets related to different political ideologies like leftist, rightist and neutral are extracted utilizing the snscrape module on basis of hashtags and usernames. Text preprocessing is done using the NLTK library involving steps like stop words removal, lemmatization and stemming. Next, the text in the tweets is transformed into usable vector forms using the count vectorizer technique. Then train test splitting is done. Then, six classification techniques namely - Multinomial-NB, Logistic-Regression, Ridge Classifier, Random Forest classifier, AdaBoostClassifier and Linear SVM are applied on the dataset. The best accuracy obtained is 0.807 with Logistic Regression.
Analysis of Data Mining Algorithms in Market Basket Analysis

Data mining is the process of sorting massive volumes of data to uncover patterns and trends that could be utilized in data analysis to help solve business problems. Due to data mining skills and equipment, businesses can help in predicting and making more informed decisions. This paper has described several aspects of data mining and data mining technology. Market basket analysis (MBA) is one of the main applications of the data mining environment that this study primarily focuses on. Marketers frequently utilise MBA, a type of analytical technique, to comprehend the purchasing habits of their customers. The goal of the MBA is to find the similarities between similar products that are bought simultaneously as well as the co-occurring patterns. The association rule technique is used to do this. By identifying the commodities that buyers commonly combine, MBA helps businesses increase their sales and marketing strategies. Additionally, this study concentrates on distinct techniques for examining particular data mining trends. Analysis of the Apriori and Frequent Pattern (FP) Growth techniques performance revealed that 10000 transactions were required to analyze completion time, and a total of 5% support was required to examine Apriori and FP-Growth with a time reduction of 65%.
Optical Character Recognition and Text to Speech Generation System using Machine Learning

A branch of computer science and artificial intelligence (AI), the study of machine learning seeks to mimic human learning and enhance accuracy over time using data and algorithms. Neural networks are now mostly employed for tasks involving pattern recognition. Picture capture of the handwritten paper and saving it in image format is an easy technique to store the data. “Optical Character Recognition” is a technique for converting handwritten data into electronic format. The science of optical character recognition makes it possible to convert different kinds of documents or photos into data that can be analyzed, edited, and retrieved. Some of the processes involved are pre-processing, segmentation, feature extraction, and post-processing. OCR has been utilized by numerous researchers to recognize characters. This project links handwritten recognition and integration and text-to-speech technology. CNN is used in this system's design to recognize the characters in a test dataset. The four steps of the suggested method are text extraction, text localization, text extraction, and text to speech conversion. The primary goal of this research is to examine CNN's capacity for character identification in the image dataset as well as the recognition accuracy after training and testing. Using multiclass Support Vector Machines (SVM), OCR is used to translate text from an image into editable text, and a Text to Speech system outputs audio.