Reviewerly/exHarmony huggingface dataset:
Classification and Application of Sports Venue Monitoring Images Using SIFT Algorithm

With the widespread use of machine learning, people pay more and more attention to computer vision. Image matching is an important research content in computer vision learning, with a wide range of applications. The purpose of this paper is based on SIFT Algorithms study the classification and application of sports venue monitoring images. The basic concept of image feature extraction and a basic process of feature extraction are introduced, which can provide a theoretical basis for feature extraction later. The SIFT algorithm and its variants are introduced, and the advantages and disadvantages of various algorithms are analyzed. The PCA-SIFT algorithm and the GPCA algorithm are introduced in detail, and the GPCA-SIFT algorithm is proposed on this basis. Compared with the PCA-SIFT algorithm, GPCA-SIFT directly reduces the dimensionality of the two-dimensional image gradient matrix, which better preserves the spatial position information between pixels. By comparing other existing feature extraction algorithms, experiments on the dataset show that the classification accuracy of the GPCA-SIFT algorithm is 96%, and the GPCA-SIFT descriptor can indeed improve the classification performance of scene images on the basis of ensuring high efficiency.
Automatic Target Recognition and Identification for Military Ground-to-Air Observation Tasks using Support Vector Machine and Information Fusion

Automatic Target Detection, Recognition, and Identification (ADTRI) is an important task, especially for the military. This paper enhances the military technique for recognizing and identifying air objects by utilizing a Support Vector Machine (SVM) combined with information fusion. For this purpose, SVM, as the recognizer, generated knowledge of 11 characteristics that consist of the Wing, Engine, Fuselage, and Tail (WEFT) of 155 military and civilian aircraft and helicopters. Then, the identification is carried out by the information fusion from the SVM result. Using the 80:20 scheme, the combination of SVM and information fusion can achieve an average accuracy of 99.60% during training and 98.39% during the testing for the combination of primary and secondary characteristics. Another important thing is that this combination can speed up the process of recognition and identification by up to 0.52 seconds.
Universal Reductions: Reductions Relative to Stateful Oracles

We define a framework for analyzing the security of cryptographic protocols that makes minimal assumptions about what a “realistic model of computation is”. In particular, whereas classical models assume that the attacker is a (perhaps non-uniform) probabilistic polynomial-time algorithm, and more recent definitional approaches also consider quantum polynomial-time algorithms, we consider an approach that is more agnostic to what computational model is physically realizable. Our notion of universal reductions models attackers as PPT algorithms having access to some arbitrary unbounded stateful Nature that cannot be rewound or restarted when queried multiple times. We also consider a more relaxed notion of universal reductions w.r.t. time-evolving, k-window, Natures that makes restrictions on Nature—roughly speaking, Nature’s behavior may depend on number of messages it has received and the content of the last $$k(\lambda )$$ -messages (but not on “older” messages). We present both impossibility results and general feasibility results for our notions, indicating to what extent the extended Church-Turing hypotheses are needed for a well-founded theory of Cryptography.
Implementation and Optimization of Distributed Stream Processing System Based on FPGA

As one of the most popular big data processing engines, Apache Storm is used in many fields. However, the performance of the traditional Storm framework can no longer meet current needs due to explosive growth of data in Internet of Things. In this article, FPGA is integrated as the computing node into the Strom framework, and two optimization methods are proposed for the framework to improve system performance. We use matrix multiplication to evaluate the optimized system. When the matrix size is 256×256, the FPGA-based Storm system has a maximum reduction of 37.10% in the average processing delay of tuples compared to the native Storm system. When the computing resources of the FPGA are large enough, the matrix size of the optimal optimization rate will also increase by increasing the parameter setting of the expansion degree.
Perception of Stakeholders on the Adoption of 4th Industrial Revolution Technologies in the Hospitality Industry in the Philippines

Due to the recent technological leaps in innovation, the hospitality industry faces paradigm shifts with the emergence of the so-called, 4th industrial revolution (4IR) which describes exponential changes to the way we live, work and relate to one another due to the adoption of cyber-physical systems, the internet of things and the internet of systems (Marr, The 4th industrial revolution is here – Are you ready? 2018). To examine the response within the hospitality sector in the Philippines, this study utilized a qualitative approach to explore the perception of stakeholders, specifically the academe, industry practitioners and government agencies in embracing changes resulting from the 4IR. Findings revealed the significant role technology has in the hotel industry in the Philippines. Participant responses indicated that although technology was perceived to be important, it cannot replace human elements necessary for hospitality. The results suggest that different stakeholders in the tourism sector such as industry professionals, academia, and members of government agencies are all willing and ready to embrace the 4IR scenario, yet not willing to let go of the human elements and interactions.
Semantic segmentation of fetal heart components in second trimester echocardiography

Fetal echocardiography has become a recommended investigation during pregnancy routine examination in the second semester of fetal development. The heart structures are now sufficiently developed to detect any indication of a possible congenital heart disease. However, the inspection of the key components is not as straightforward for the less experienced young obstetricians. Moreover, a background automatic check of the presence of the main elements could be helpful in speeding up the overall assessment during the periodic pregnancy control. In this respect, the current paper proposes the support of a deep learning model towards the semantic segmentation of eleven key structures that need to be identified within the second trimester fetal heart scans.
Application of Hawk-Eye Technology to Sports Events

Centered upon the Olympic motto “Higher, Stronger, Faster - Together”, competitive sports in modern days are developing rapidly. With the increasing intensity of competitions, the speed of balls gets faster, and so does the movement of athletes. This may lead to misjudgment made by referees, bringing about negative impacts on the game. This is where Hawk-Eye comes in. This computer vision system visually tracks the trajectory of the ball, whose video will be captured by high-speed cameras and reviewed in 3D graphics by a Simultaneous Multi-Agent Recurrent Trajectory system (SMART). This will assist referees' judgment and add more fairness and impartiality to the game, echoing the Olympic spirit. It is possible to expand the application of Hawk-Eye technology in sports, and it is also suggested that the data processing of this system should be further optimized by means of positioning more cameras on different angles and locations in order to achieve more data, enhance the processing capability, and shorten the time for calculating the ball's trajectory. When the number of errors is reduced, the time needed to settle disputes between referees will also be decreased, and Hawk-Eye system will work smoothly with referees. In this way, the fairness of sports game can be ensured and interruptions be largely diminished, allowing the audience to better enjoy the game and feel the charm and pleasure of competitive sports.
Improvement of Path Planning Algorithm based on Small Step Artificial Potential Field Method

In the process of controlling the behavior of multi-agent, the cooperative control between the agent obstacle avoidance algorithm and the agent is a necessary link to realize the task of multi-agent. Based on the artificial potential field method, this paper conducts formation obstacle avoidance control for multi-agent formations, analyzes the influence of its step size on path planning, and proposes two methods to optimize the path and analyze its advantages and disadvantages. Finally, the sampling path is quality checked and re-optimized by the informed RRT* algorithm.
Jaebot: Discord Bot for Network Analysis with NetworkX

Network Analysis is the process of investigating social structure through the use of networks and graph theory. In this research NetworkX is integrated in to a social networking platform Discord, to help visualize connections made in a server. NetworkX is also equipped with centralities that can be used to find out how influential a member of the discord server is, the centralities that will be used in this research are degree, betweenness, closeness and eigenvector centrality. Initial research on a focus group was done to gather data to compare with the results and a hypothesis was create. It was believed that the more mentions someone sent to another, the closer their relationship is. The result of implementing the NetworkX centralities was able to show the top three users that are the most influential in the server channel.
Forensic Analysis of WhatsApp chat data

Due to the widespread use of mobile phones and wireless applications, there is unquestionably a surge in cybercrimes. As a result, there is an urgent need for mobile forensics. The investigators should decide one of the three gathering types—manual, logical, or physical—to use before starting an investigation. The existing mobile acquisition technologies exploit these in various ways to create images of all mobile content, files of particular data categories, or data from a particular application. The tool may capture more data than necessary, forcing researchers to individually filter data may, or it may obtain all of the data, but this does not help with the examination of instances involving a particular mobile application. Data from the application without adequate analysis both in instances need a lot of work and time. This study compares and analyses forensics presently accessible and gives a demonstrated view of available tools that can perform data extraction and forensic investigations on WhatsApp. This study also discusses the mechanism behind the messenger technology, protocols behind the technology, and different ways to extract the data from the mobile phone to perform forensic analysis. Additionally, this study focuses on the weak points of the tools technology and suggests better methods to improve the vast field of WhatsApp forensics.
Natural Language Processing of Movie Reviews to Detect the Sentiments using Novel Bidirectional Encoder Representation-BERT for Transformers over Support Vector Machine

Aim:The aim of the study is to detect sentiment analysis from the good ones and improve the false positivity rate by using the proposed Novel Bidirectional Encoder Representation for Transformers-(Novel BERT) over Support Vector Machine-SVM.Materials and Methods: Sample groups that are considered in the project can be classified into two, one for Novel BERT over SVM , which are tested using 0.80 for G-power to determine the sample size and for t-test analysis.25000 IMDB movie reviews dataset that data collected from Twitter.Results: The automatic feature selection of the BERT algorithm splits the data with best fit, which has an average accuracy of 83.5%, which by far seems to be better than the SVM which gives average accuracy 75.3%.The significance is around 0.042 (p<0.05) and therefore there is a statistically insignificant difference among the study group.Conclusion: BERT seems to be better in finding the Sentiment in IMDB movie dataset over the SVM algorithm.
Enabling Secure Deduplication in Encrypted Decentralized Storage

With the rapid development of blockchain technology, decentralized cloud storage services are emerging and have been a storage new option in this era. They aim to leverage the unused storage resources across the network to build a more economical and reliable distributed storage network and thus eliminate the trust in the centralized storage providers via matured blockchain consensus mechanisms. However, current solutions either lack the protection of user data privacy or apply conventional encryption methods that cannot support cross-user deduplication over encrypted data. These limitations make them struggle to balance the need for optimized storage space utilization and encrypted data protection, especially in the scenario where the user’s files are geographically distributed in different nodes around the world. In this paper, we propose a secure deduplication system in the context of encrypted decentralized cloud storage. It utilizes smart contract to incorporate the message-locked encryption (MLE) scheme, the most prominent cryptographic primitive in secure data deduplication. With a carefully tailored design, our proposed scheme can be seamlessly deployed to the public blockchain with transparency. Together, our design enables secure data deduplication over decentralized storage, while providing stringent cryptographic data privacy guarantees. In particular, our proposed design has a natural benefit to prevent potential malicious attacks such as file ownership cheating and file ciphertext poisoning. We implement a prototype of our system and deploy it to Ethereum. Comprehensive performance evaluations are conducted with real datasets to demonstrate the effectiveness and efficiency of our design.
Provable Chaotically Authenticated Encrypted Biomedical Image Using OFDM Transmission

In this research, a unique multiband random chaotic key generator based provable authenticated encrypted technique for biomedical picture for the healthcare biomedical system, which can be used in 5G communication system is presented. In addition, the encryption method employed in this research is based on Multiband Random Chaotic Key Generator, and the proposed provable authenticated methodology is based on symmetric authenticated encryption data (MBRCKG). In the proposed proven Orthogonal Frequency Division Multiplexing (OFDM) communication system, the Authenticated Chaotic Encrypted Biomedical Image (ACE-BI) is utilized. This study uses discrete wavelet transformation (DWT) and discrete cosine transformation (DCT) to mask patient data and hospital watermarks in biological images. With various statistical and OFDM settings, channel analysis and statistical analysis have been examined for their effects on the collected hospital logo and patient data. The simulation studies demonstrate how resistant to communication signal processing the proposed ACE-BI method is. Additionally, the proposed algorithm is able to reduce encryption time to one quarter because the partial encryption based in one level DWT scheme.
PESMOD: Small Moving Object Detection Benchmark Dataset for Moving Cameras

This paper presents a new high resolution aerial images dataset in which moving objects are labelled manually. It aims to contribute to the evaluation of the moving object detection methods for moving cameras. The problem of recognizing moving objects from aerial images is one of the important issues in computer vision. The biggest problem in the images taken by Unmanned Aerial Vehicles(UAV) is that the background is constantly variable due to camera movement. There are various datasets to evaluate motion detection methods in the literature. The prepared dataset consists of challenging and high resolution images containing small targets compared to other datasets. The dataset consists of different images such as people walking in nature, people skiing, vehicles driving through the forest, towns and vehicles going on the highway. This study also represents a post-processing method that can be used after background modeling for moving object detection. Experimental studies show that proposed post-processing technique provides a significant increase in precision and F-score metrics. The results of different methods in the literature are compared in detailed for each sequence in the dataset. The dataset and source codes are available at: http://github.com/mribrahim/PESMOD
Vehicle Re-identification via Spatio-temporal Multi-instance Learning

Vehicle re-identification is a cross-camera vehicle retrieval method. Compared with the method of manually retrieving surveillance video to realize vehicle re-identification, the method based on deep learning uses computer to achieve cross-camera matching of target vehicles, which saves labor costs, so it has high practical application value in the field of intelligent transportation. Common vehicle re-identification algorithms achieve re-identification by making the characteristics of different pictures of the same id vehicle tend to be consistent. Generally, these methods rely on manually annotated datasets. However, the accuracy of the model may decrease due to the possibility of labeling errors when manually labeling datasets, especially large-scale datasets. To solve the above problems, this paper proposes a vehicle re-identification algorithm based on spatio-temporal multi-instance learning. It uses a multi-instance bag to train a feature extraction model and pays attention to the features of the entire multi-instance bag and ignores the features of a single instance. So it can handle the problem of mislabeling in the dataset. Experimental results show that the model is feasible: on the VeRi dataset, the model can achieve 33.3% mAP and 67.9% Rank-1 accuracy.
Logic-based AI for Interpretable Board Game Winner Prediction with Tsetlin Machine

Hex is a turn-based two-player connection game with a high branching factor, making the game arbitrarily complex with increasing board sizes. As such, top-performing algorithms for playing Hex rely on accurate evaluation of board positions using neural networks. However, the limited interpretability of neural networks is problematic when the user wants to understand the reasoning behind the predictions made. In this paper, we propose to use propositional logic expressions to describe winning and losing board game positions, facilitating precise visual interpretation. We employ a Tsetlin Machine (TM) to learn these expressions from previously played games, describing where pieces must be located or not located for a board position to be strong. Extensive experiments on <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$6\times 6$</tex> boards compare our TM-based solution with popular machine learning algorithms like XGBoost, InterpretML, decision trees, and neural networks, considering various board configurations with 2 to 22 moves played. On average, the TM testing accuracy is 92.1%, outperforming all the other evaluated algorithms. We further demonstrate the global interpretation of the logical expressions, and map them down to particular board game configurations to investigate local interpretability. We believe the resulting interpretability establishes building blocks for accurate assistive AI and human-AI collaboration, also for more complex prediction tasks.
On the Premise of a Swarm Guidance Ontology for Human-Swarm Teaming

Effective Human-Swarm Teaming (HST) relies on bi-directional information flow between the human and the swarm. Systems with human control or oversight rely on information flow from the swarm to the humans to inform decisions, while information that flows back from humans is only that necessary for actuation, which remains primarily physical. To unlock the full potential of HSTs, the augmentation must extend into the overall logic of teaming, including both the human’s and machine’s cognitive domains, whereby an AI-equipped robot teammate is capable of complex cognitive functions. The effectiveness of HST will need a sufficient level of transparency in the interaction space formed by the bi-directional information flow between the human and the swarm. This transparency must continuously and constructively interpret the information exchanged between the human and the swarm to afford both cognitive agents with the capacity to form shared understanding and situation awareness, and thus, facilitating effective teaming through trust. An ontology is one formal representational construct that enables bi-directional interpretation, thus, transparency. In this paper, we conceptualise and present a meta-ontology for transparent HST interactions.
HawkEye-HMI-Generation: A Method to Synthesize Zoomable Process Automation User Interfaces

Process plant operators use graphical user interfaces to supervise complex processes with tens of thousands of instruments. Such systems may require more than 1000 operator screens, but an operator can only view 3-4 screens simultaneously ("keyhole-effect"), thus prolonging root cause analysis and emergency reactions. This effect contributes to the billions of USD lost in the process industry every year due to unscheduled downtimes. Researchers have proposed alternative, zoomable 2D user interfaces (“HawkEye HMIs”) similar to Google Maps, to address the keyhole-effect, but manual efforts to create these HMIs proved to be cost-prohibitive. This paper proposes a novel method for HawkEye-HMI-Generation based on semi-automated page layouting and automated level-of-detail zooming, which can significantly lower graphics engineering costs. We tested a prototypical implementation on two cases from large production plants with up to 90 process graphics and more than 5000 graphical nodes, demonstrating high scalability, good usability, and a more than 70 percent cost savings potential.
Urgent Text Detection in Bengali Language Based on Boosting Techniques

This paper presents a learning approach on a unique dataset formulated by authors that detects urgent texts from the posts on social media platforms in Bengali language. It is difficult to keep track of every information we go through social media. In the collision of numerous posts, it is easy to miss information that is urgent. In this advanced era of machine learning, detecting urgent texts among thousands of posts would be much easier if we can implement a model that can filter the urgent text out of them. Therefore, we propose an approach that can identify any type of urgent texts from public posts by leveraging a manually constructed dataset that is fully human annotated. Apart from traditional machine learning classifiers, we applied boosting algorithms in our proposed method in addition. Experimentally, a significant increase in accuracy has been noticed by boosting weak learners. Support Vector Machine (SVM) achieved 80.9% accuracy where gradient boosting outperformed the traditional approach with 82% accuracy while detecting urgent texts in Bengali language.