Reviewerly/exHarmony huggingface dataset:
Attention-guided lightweight generative adversarial network for low-light image enhancement in maritime video surveillance

Abstract Benefiting from video surveillance systems that provide real-time traffic conditions, automatic vessel detection has become an indispensable part of the maritime surveillance system. However, high-level vision tasks generally rely on high-quality images. Affected by the imaging environment, maritime images taken under poor lighting conditions easily suffer from heavy noise and colour distortion. Such degraded images may interfere with the analysis of maritime video by regulatory agencies, such as vessel detection, recognition and tracking. To improve the accuracy and robustness of detection accuracy, we propose a lightweight generative adversarial network (LGAN) to enhance maritime images under low-light conditions. The LGAN uses an attention mechanism to locally enhance low-light images and prevent overexposure. Both mixed loss functions and local discriminator are then adopted to reduce loss of detail and improve image quality. Meanwhile, to satisfy the demand for real-time enhancement of low-light maritime images, model compression strategy is exploited to enhance images efficiently while reducing the network parameters. Experiments on synthetic and realistic images indicate that the proposed LGAN can effectively enhance low-light images with better preservation of detail and visual quality than other competing methods.
Image Analysis Method of Substation Equipment Based on Convolutional Neural Network

Due to the continuous development of computer technology to promote the continuous progress of substation automation technology, the current substation equipment is diverse and there are many interferences, making the accuracy of the image processing algorithm to be low, and there is a lack of a complete automatic processing system. Convolutional neural networks (CNNs) are one of the most important breakthroughs in artificial intelligence in the last decade, especially in the field of image recognition, and have made important research achievements. In this study, we apply CNNs to substation equipment image processing, a method that performs feature extraction for recognition through substation equipment images. The research focuses on the expansion of the image sample set, the automatic training method based on recognition rate, and the voting strategy based on integrated learning, which not only improves the training efficiency of the model but also increases the recognition rate, and the proposed method is of high practicality.
No means ‘No’: a non-improper modeling approach, with embedded speculative context

The medical data are complex in nature as terms that appear in records usually appear in different contexts. Through this article, we investigate various bio model's embeddings (BioBERT, BioELECTRA and PubMedBERT) on their understanding of 'negation and speculation context' wherein we found that these models were unable to differentiate 'negated context' versus 'non-negated context'. To measure the understanding of models, we used cosine similarity scores of negated sentence embeddings versus non-negated sentence embeddings pairs. For improving these models, we introduce a generic super tuning approach to enhance the embeddings on 'negation and speculation context' by utilizing a synthesized dataset.After super-tuning the models, we can see that the model's embeddings are now understanding negative and speculative contexts much better. Furthermore, we fine-tuned the super-tuned models on various tasks and we found that the model has outperformed the previous models and achieved state-of-the-art on negation, speculation cue and scope detection tasks on BioScope abstracts and Sherlock dataset. We also confirmed that our approach had a very minimal trade-off in the performance of the model in other tasks like natural language inference after super-tuning.The source code, data and the models are available at: https://github.com/comprehend/engg-ai-research/tree/uncertainty-super-tuning.
Design and realization of an aeronautical cleaning robot for aircraft maintenance 4.0 based on artificial intelligence

Preventive and corrective maintenance of aircraft are key elements in the aviation industry, with the aim of keeping the aircraft in good condition and available to personnel when they are needed. To do this, maintenance operations must be carried out rigorously and with great precision. The cleaning of an aircraft is a critical task in the preventive maintenance of the aircraft. This operation requires a human intervention after each stopover which causes unexpected material failures such as shocks on the fuselage in addition to a bad execution of the operation of cleaning of the seats evoking problems between the crew of the aircraft and the teams of cleaning, this causes a delay in the planning of the flights and financial overloads for the airline companies. In this study, we presented the design and realization of a cleaning robot that performs most of the tasks done by the cleaning teams such as vacuuming the carpet and collecting large waste. This robot is equipped with a camera and an artificial intelligence program installed on a raspbery4 board. We expect robotics to be one of the keys to replacing humans in the heart of production facilities in the Factory of the Future. As they can perform their tasks with infinite perfection in a precise time. Preventive and corrective maintenance of aircraft are key elements in the aviation industry, with the aim of keeping the aircraft in good condition and available to personnel when they are needed. To do this, maintenance operations must be carried out rigorously and with great precision. The cleaning of an aircraft is a critical task in the preventive maintenance of the aircraft. This operation requires a human intervention after each stopover which causes unexpected material failures such as shocks on the fuselage in addition to a bad execution of the operation of cleaning of the seats evoking problems between the crew of the aircraft and the teams of cleaning, this causes a delay in the planning of the flights and financial overloads for the airline companies.
A Systematic Study on the Extraction and Image Reproduction of Ceramic Sculpture Artworks

In order to solve the research on the extraction of ceramic sculpture artwork patterns, and, in the process of image reproduction, the problem of too few feature points in the object image, the author proposes an image stitching algorithm that combines SIFT and MSER algorithms. After comprehensively analyzing the principles, advantages, and disadvantages of the current main image stitching methods, in terms of feature matching, based on the K-D tree search algorithm, the improved BBF algorithm is used to improve the search efficiency of feature points. In order to remove the possible cracks in the stitching process, an improved multiband fusion algorithm is used to seamlessly stitch the registered images. The results show that the feature points detected by the one-dimensional normal distribution algorithm are on average 0.1%, 0.5%, 1.7%, 4.4%, and 9.2%. The algorithm combining SIFT and MSER to extract feature points can reach 3.6%, 4.6%, 8.4%, 15%, and 19.1%. The experimental results show that the algorithm proposed by the author can extract more image feature points to facilitate later image registration. The image blur phenomenon in the original image fusion algorithm is solved, and a complete and clear two-dimensional plane pattern is finally obtained.
Improved color image defogging algorithm based on dark channel prior

An improved algorithm of image defogging was proposed based on dark channel prior in order to solve the low efficiency and color distortion in the bright area using original algorithm. If the image contains large areas of bright areas such as sky, white clouds or partial white objects and water surface, we can know that the dark channel prior theory does not apply to these areas. Firstly, it is necessary to clear the bright area of the image. According to principle that he adjacent pixel attributes have similarity, the image transmittance of the local region also has similarity, Block function is Consruted. Applied the dark channel prior, judging whether each block includes a bright area by the absolute value of difference of atmospheric intensity and dark channel, the dark and bright areas of the image are obtained. So the estimation value of the adaptive space transmittance are also obtained. Secondly, the transmittance of bright region is small and it causes deviation, so the enhancement formula is used to modify it dynamically. In order to preserve the edge details after image restoration, for bright areas, using texture function to optimize transmittance independently, for others, using gradient and texture function together. Finally, it restored the fog-free image applying the atmospheric scattering model. The experimental results showed that the restored image had obvious details and rich color and fast processing speed through the proposed algorithm. The algorithm can also be applied to outdoor visual systems, such as video surveillance, intelligent traffic and so on.
Building space for feminist solidarity

In this forum we explore different perspectives for how to apply intersectionality as a critical framework for design across multiple contexts. --- Yolanda A. Rankin and Jakita O. Thomas, Editors
Between metaphor and meaning

research-article Open Access Share on Between metaphor and meaning: AI and being human Author: Maya Indira Ganesh University of Cambridge University of CambridgeView Profile Authors Info & Claims InteractionsVolume 29Issue 5September - October 2022 pp 58–62https://doi.org/10.1145/3551669Published:30 August 2022Publication History 0citation2,525DownloadsMetricsTotal Citations0Total Downloads2,525Last 12 Months2,525Last 6 weeks2,221 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
A Dual-Robot Cooperative Welding Path Planning Algorithm Based on Improved Ant Colony Optimization

In this paper a novel path planning algorithm based on improved ant colony optimization is proposed for dual-robot cooperative welding. We seek to provide a low-cost, fast and effective solution for such problems. The optimization model of dual-robot collaborative welding is established based on actual welding process constraints and shortest welding path target. Then, a dynamic transfer strategy and information update strategy are proposed to improve the ant colony algorithm (ACO+) based on this model. Finally, the proposed ACO+ algorithm is implemented to solve the dual-robot path planning problem in the welding task of a large complex component. The simulation demonstrates that the proposed method has faster convergence rate and shorter welding path compared with the unmodified ant colony algorithm. Particularly, this algorithm is suitable for path planning of dual-robot cooperative welding for symmetrical complex component.
On Primitive Recursive Characteristics of Chess

Several characteristics of chess are investigated with methods of computability and number theories. It is shown that for an unfinished game it is primitive recursively decidable whether the game is winnable, drawable, or absolutely losable within a specified number of future moves for the player whose turn it is to play on the last board of the game. It is also shown that there exist primitive recursive procedures to compute optimal continuations of unfinished games within specified numbers of future moves and that the set of chess games is recursive.
Correcting for digital image correlation speckle inversion at high temperature using color cameras

Digital image correlation (DIC) is a popular, noncontacting technique to measure full-field deformation by using cameras to track the motion of an applied surface pattern. Because it is noncontacting, DIC can be performed for extreme temperature applications (e.g., hot-fire rocket testing of carbon composite rocket nozzles) under harsh conditions during which bonded gauges are damaged. Speckle pattern inversion is a phenomenon that sometimes occurs while performing high-temperature DIC. During speckle pattern inversion, portions of the surface pattern that were initially darker at room temperature (e.g., graphite) may emit more light due to blackbody radiation than the portions that were initially paler, thereby producing images in which the pattern appears inverted at high temperature relative to the initial pattern at room temperature. This phenomenon can prevent the correlation algorithm from being able to resolve the displacements between images. This work compares three methods to mitigate speckle pattern inversion: (A) the subtraction method, a recently-published technique in which two high-temperature images are subtracted to remove unwanted light; (B) the filtering method, a popular technique in which optical bandpass filters screen out unwanted light; and (C) the histogram rescaling method, a proposed new method that pairs a color camera with a blue light source and uses information from the green sensor of the camera to correct against inversion in the blue sensor through postprocessing. The histogram rescaling method is shown to successfully eliminate speckle pattern inversion and has the added advantages that it does not require quasi-static loading to be able to compensate for speckle pattern inversion, nor does it impose thick-glass distortions caused by the optical filter.
Implementation of IoT-Based Health Care and Saline Monitoring System Using Arduino UNO

In this twenty-first century, automation is the new era of modern machines. The new medical industry has gone modernized but with some problems for which we delve into the topic of what kinds of issues people confront in hospitals and how smart technologies help or are meant to help doctors and nurses tackle these issues. One of the most typical issues that doctors face is that when a patient is sick, then the doctor must first administer saline to the patient. When the saline bottle is in place, they must have to check frequently the level of the bottle. It is impossible to keep track of everything all of the time because there are fewer doctors in underdeveloping nations like India, and it can be difficult for a single doctor to supervise multiple patients at the same time. The demand for health care has increased dramatically in recent decades, necessitating the development of well-equipped, efficient monitoring systems for health care facilities, which will benefit doctors and ensure that patients are protected from any type of health problem while undergoing treatment. Using IoT, we have introduced this solution for the benefit of the medical industry.
‘Right to Be Forgotten’: Analyzing the Impact of Forgetting Data Using K-NN Algorithm in Data Stream Learning

New international regulations concerning personal management data guarantee the ‘Right to Be Forgotten’. One might request to have their data erased from third-party tools and services. This requirement is especially challenging when considering the behavior of machine learning estimators that will need to forget portions of their knowledge. In this paper, we investigate the impact of these learning and forgetting policies in data stream learning. In data stream mining, the sheer volume of instances typically makes it unfeasible to store the data or retraining the learning models from scratch. Hence, more efficient solutions are needed to deal with the dynamic nature of online machine learning. We modify an incremental k-NN classifier to enable it to erase its past data and we also investigate the impact of data forgetting in the obtained predictive performance. Our proposal is compared against the original k-NN algorithm using seven non-stationary stream datasets. Our results show that the forgetting-enabled algorithm can achieve similar prediction patterns compared to the vanilla one, although it yields lower predictive performance at the beginning of the learning process. Such a scenario is a typical cold-start behavior often observed in data stream mining applications, and not necessarily related to the employed forgetting mechanisms.
The Future of Blockchain

Blockchain's current uses demonstrate potential for enhancing efficiencies and patient-centered solutions in life sciences research. For blockchain to continue to present new features and remain relevant in life sciences research, it is critical for blockchain capabilities to evolve and integrate with newer technologies. This chapter introduces the role of blockchain technologies in smart data, quantum computing, digital twins, and the emergence of the metaverse. Additional predictions and recommendations for preparing for future blockchain needs are provided.
SASP: a Semantic web-based Approach for management of Sharable cybersecurity Playbooks

In incident management, response and recovery actions are designed to effectively mitigate ongoing or future cyberattacks. A security playbook consists of a pipeline of instructions to document necessary response and recovery actions to deal with a specific type of incident. Since many organisations lack the resources, expertise and know-how to handle incidents, sharing playbooks across organisations could significantly improve their response capabilities against cyberattacks. However, playbooks are often organisation specific and usually not machine-readable, sharable and interoperable. In this work, we propose a semantic web-based approach to capture the knowledge of incident response and recovery steps to support sharing of playbooks based on a standardised and common vocabulary. To further demonstrate our approach, we introduce SASP, a proof-of-concept tool based on Semantic MediaWiki for playbook management. In this paper, we describe the key requirements from incident handlers to share playbooks, SASP architecture design, and its core components and functionalities. We then discuss the results of our user-centric evaluation conducted on members of different Security Operation Centres and the further potential of the solution.
Data Acquisition on a Large Darknet Marketplace

Darknet marketplaces in the Tor network are popular places to anonymously buy and sell various kinds of illegal goods. Previous research on marketplaces ranged from analyses of type, availability and quality of goods to methods for identifying users. Although many darknet marketplaces exist, their lifespan is usually short, especially for very popular marketplaces that are in focus of law enforcement agencies.
Self-supervised Contrastive Learning for Predicting Game Strategies

Many games enjoyed by players primarily consist of a matching system that allows the player to cooperate or compete with other players with similar scores. However, the method of matching only the play score can easily lose interest because it does not consider the opponent’s playstyle or strategy. In this study, we propose a self-supervised contrastive learning framework that can enhance the understanding of game replay data to create a more sophisticated matching system. We use actor-critic-based reinforcement learning agents to collect many replay data. We define a positive pair and negative examples to perform contrastive learning. Positive pair is defined by sampling from the frames of the same replay data, otherwise negatives. To evaluate the performance of the proposed framework, we use Facebook ELF, a real-time strategy game, to collect replay data and extract data features from pre-trained neural networks. Furthermore, we apply k-means clustering with the extracted features to visually demonstrate that different play patterns and proficiencies can be clustered appropriately. We present our clustering results on replay data and show that the proposed framework understands the nature of the data with consecutive frames.
T5W: A Paraphrasing Approach to Oversampling for Imbalanced Text Classification

Imbalanced datasets are datasets with one or more underrepresented classes when compared to other classes. Such datasets pose problems during classification due to the lack of sufficient data to train minority classes. To handle this imbalance in text data, this paper proposes an oversampling technique that uses a combination of the T5 Transformer and the WordNet corpus to balance the dataset by paraphrasing text in minority classes. The WordNet corpus is used to extract synonyms of "relevant" words. Substituting these synonyms in the sentences augments the database by increasing data in minority classes. Combining these augmented sentences with the paraphrased sentences extracted using the T5 Transformer results in a larger and balanced dataset. Standard classifiers such as the Logistic Regression algorithm are used to compare the performance metrics before and after resampling the dataset. The results show that oversampling using the proposed approach significantly improves the performance of text classification algorithms. To automate the task of oversampling using a paraphrasing tool, the integration of the model with a Robotic Process Automation tool is detailed.
A resolution enhancement plug-in for deformable registration of medical images

Image registration is a fundamental task for medical imaging. Resampling of the intensity values is required during registration and better spatial resolution with finer and sharper structures can improve the resampling performance and hence the registration accuracy. Super-resolution (SR) is an algorithmic technique targeting at spatial resolution enhancement to achieve an image resolution beyond the hardware limitation. In this work, we consider SR as a preprocessing technique and present a CNN-based lightweight resolution enhancement module (REM) which can be easily plugged into the registration networks in a cascaded manner. Different residual schemes and network configurations of REM are investigated to obtain an effective architecture design. Besides, an auxiliary loss is introduced into the cascaded network to empower multi-hierarchical supervision and strengthen the fidelity of the output. In fact, REM is not confined to image registration, it can also be straightforwardly integrated into other vision tasks for enhanced resolution. In the experiments, REM and the cascaded registration network are evaluated on Brain MR images quantitatively and qualitatively at different upscaling factors. It is shown that REM not only improves the registration accuracy, especially when the input images severely suffer from the degraded spatial resolution, but also reconstructs resolution enhanced images which can be exploited for successive diagnosis.