Reviewerly/exHarmony huggingface dataset:
Advanced Object Enhancement in an Image

Inclement dusty weather can have a substantial impact on image quality, as capturing photographs in such conditions frequently results in unwanted degradation such as weak contrast, insufficient colors, or color cast. Typically, images taken in dusty conditions have a color shift toward a brown hue, orange, or opaque yellow. As a result, such undesired artefacts must be efficiently handled in order for collected images to be more dependable for subsequent interpretations. As a result, image processing and computer vision applications such as surveillance, intelligent transportation systems, motion detection, object recognition, and object tracking, to mention a few, might be affected by such aberrations. Furthermore, these abnormalities can make it difficult to see relevant information in the collected photos. In this paper, the proposed method helps to enhance the quality of image by adjusting the image parameters and makes it useful for further interpretations. The PSNR range for an ideal image should be between 30 and 50. By altering the tuning parameter which is used to control color fidelity of image and calculating the intensification factors by obtaining the thresholding limit and membership function for tuning. The tuning outputs are concatenated to get the desired PSNR value of image.
Books in a bubble.

Open access infrastructure for books is becoming more mature, and it is being used by an increasing number of people. The growing importance of open access infrastructure leads to more interest in sustainability, governance and impact assessment. The assessment of the OAPEN Library – containing over 20,000 titles – fits within this trend. How well does the collections meets the needs of its stakeholders: readers, libraries, funders and publishers? The composition of the collection is measured using subject and language. Both dissemination and the content-related aspects are paired to the number of publications. The average number of downloads per title is relatively similar for all subjects. However, the mean downloads of titles in English is roughly twice as much compared to German and the other languages. Combining subjects and languages shows that the dissemination of books in languages other than English is less predictable. This assessment has illustrated the composition of the collection and how its readers make use of it. The visualisation helped to tell a complicated story in a simple way; a powerful instrument to guide the further development of this open access infrastructure.
An answer recommendation framework for an online cancer community forum

Health community forums are a kind of online platform to discuss various matters related to management of illness. People are increasingly searching for answers online, particularly when they are diagnosed with cancer like life-threatening diseases. People seek suggestions or advice through these platforms to make decisions during their treatments. However, locating the correct information or similar people is often a great challenge for them. In this scenario, this paper proposes an answer recommendation system in an online breast cancer community forum that provide guidance and valuable references to users while making decisions. The answer is the summary of already discussed topic in the forum, so that they do not need to go through all the answer posts which spans over multiple pages or initiate a thread once again. There are three phases for the answer recommendation system, including query similarity model to retrieve the past similar query, query-answer pair generation and answer recommendation. Query similarity model is employed by a Siamese network with Bi-LSTM architecture which could achieve an F1-score of 85.5%. Also, the paper shows the efficacy of transfer learning technique to generalize the model well in our breast cancer query-query pair data set. The query-answer pairs are generated by an extractive summarization technique that is based on an optimization algorithm. The effectiveness of the generated summary is evaluated based on a manually generated summary, and the result shows a ROUGE-1 score of 49%.
A Novel Robust IMM Filtering Method for Surface-Maneuvering Target Tracking with Random Measurement Delay

A proper filtering method for jump Markov system (JMS) is an effective approach for tracking a maneuvering target. Since the coexisting of heavy-tailed measurement noises (HTMNs) and one-step random measurement delay (OSRMD) in the complex scenarios of the surface maneuvering target tracking, the effectiveness of typical interacting multiple model (IMM) techniques may decline severely. To solve the state estimation problem in JMSs with HTMN and OSRMD simultaneously, this article designs a novel robust IMM filter utilizing the variational Bayesian (VB) inference framework. This algorithm models the HTMNs as student’s t-distribuitons, and presents a random Bernoulli variable to describe the OSRMD in JMSs. By transforming measurement likelihood function form from weighted summation to exponential product, this paper constructs hierarchical Gaussian state space models. Then, the state vectors, random Bernoulli vairable, and model probability are inferred jointly according to VB inference. The surface maneuvering target tracking simulation example result indicates that the presented IMM filter achieves superior target state estimation accuracy among existing IMM filters.
Detection and Analysis of Ethereum Energy Smart Contracts

As blockchain technology advances, so has the deployment of smart contracts on blockchain platforms, making it exceedingly challenging for users to explicitly identify application services. Unlike traditional contracts, smart contracts are not written in a natural language, making it difficult to determine their provenance. Automatic classification of smart contracts offers blockchain users keyword-based contract queries and a streamlined effective management of smart contracts. In addition, the advancement in smart contracts is accompanied by security challenges, which are generally caused by domain-specific security breaches in smart contract implementation. The development of secure and reliable smart contracts can be extremely challenging due to domain-specific vulnerabilities and constraints associated with various business logics. Accordingly, contract classification based on the application domain and the transaction context offers greater insight into the syntactic and semantic properties of that class. However, despite initial attempts at classifying Ethereum smart contracts, there has been no research on the identification of smart contracts deployed in transactive energy systems for energy exchange purposes. In this article, in response to the widely recognized prospects of blockchain-enabled smart contracts towards an economical and transparent energy sector, we propose a methodology for the detection and analysis of energy smart contracts. First, smart contracts are parsed by transforming code elements into vectors that encapsulate the semantic and syntactic characteristics of each term. This generates a corpus of annotated text as a balanced, representative collection of terms in energy contracts. The use of a domain corpus builder as an embedding layer to annotate energy smart contracts in conjunction with machine learning models results in a classification accuracy of 98.34%. Subsequently, a source code analysis scheme is applied to identified energy contracts to uncover patterns in code segment distribution, predominant adoption of certain functions, and recurring contracts across the Ethereum network.
Long-Tailed Metrics and Object Detection in Camera Trap Datasets

With their advantages in wildlife surveys and biodiversity monitoring, camera traps are widely used, and have been used to gather massive amounts of animal images and videos. The application of deep learning techniques has greatly promoted the analysis and utilization of camera trap data in biodiversity management and conservation. However, the long-tailed distribution of the camera trap dataset can degrade the deep learning performance. In this study, for the first time, we quantified the long-tailedness of class and object/box-level scale imbalance of camera trap datasets. In the camera trap dataset, the imbalance problem is prevalent and severe, in terms of class and object/box-level scale. The camera trap dataset has worse object/box-level scale imbalance, and too few samples of small objects, making deep learning more challenging. Furthermore, we used the BatchFormer module to exploit sample relationships, and improved the performance of the general object detection model, DINO, by up to 2.9% and up to 3.3% in terms of class imbalance and object/box-level scale imbalance. The experimental results showed that the sample relationship was simple and effective, improving detection performance in terms of class and object/box-level scale imbalance, but that it could not make up for the low number of small objects in the camera trap dataset.
The Effect of Blue Noise on the Optimization Ability of Hopfield Neural Network

Noise is ubiquitous in the real-world environment. At present, most scholars only include the stage of Gaussian white noise when applying noise in neural networks and regard white noise as a tool to optimize the network model, which is far from enough, because noise not only affects the optimization ability of the Hopfield neural network but can also better fit the needs of the actual use of the scene. Therefore, according to the problems in the existing research, a method is proposed to combine the neural network with colored noise according to the signal-to-noise ratio. Taking blue noise as an example, the anti-interference ability of the Hopfield neural network regarding colored noise is studied. The results show that for the Hopfield neural network driven by blue noise, by adjusting the neural network step size, excitation function and signal-to-noise ratio, it not only provides ideas for adding colored noise to the neural network but also enables the neural network model to have better optimization-seeking ability. The research results have some reference significance for improving the practical application of neural networks in noisy environments.
Creating an Interactive Urban Traffic System for the Simulation of Different Traffic Scenarios

The social and political efforts to fight climate change have contributed to a re-thinking of traffic systems, especially in urban areas under constant transformation. To simulate and visualize planning scenarios of urban traffic systems in a realistic way, the possibilities of virtual 3D environments have regularly been used. The modern potentials of (immersive) virtual reality, however, still require exploration, evaluation, and further development. Using the game engine Unity, an immersive virtual environment was developed to visualize and experience dynamic traffic conditions of a highly dense urban area. The case study is based on the characteristic model of a Central European city (not a representation of a real city), which brings together the specific considerations of urban traffic, such as mirroring the complex interplay of pedestrians as well as individual and public transport. This contribution has an applied methodological focus and considers possibilities as well as difficulties in the design of a reliably running (open-end) traffic system. The applied tool for the creation of a modular and customizable traffic system in Unity resulted in a traffic system that is capable of reacting to the individual behavior of the user (including the individualized motion of the avatar), without leading to accidents or uncorrectable traffic jams. Therefore, the tool used could be a valuable option for any developer of immersive virtual environments in Unity to equip these immersive virtual environments with a traffic system, without the use of additional third-party software.
Better Safe Than Never: A Survey on Adversarial Machine Learning Applications towards IoT Environment

Internet of Things (IoT) technologies serve as a backbone of cutting-edge intelligent systems. Machine Learning (ML) paradigms have been adopted within IoT environments to exploit their capabilities to mine complex patterns. Despite the reported promising results, ML-based solutions exhibit several security vulnerabilities and threats. Specifically, Adversarial Machine Learning (AML) attacks can drastically impact the performance of ML models. It also represents a promising research field that typically promotes novel techniques to generate and/or defend against Adversarial Examples (AE) attacks. In this work, a comprehensive survey on AML attack and defense techniques is conducted for the years 2018–2022. The article investigates the employment of AML techniques to enhance intrusion detection performance within the IoT context. Additionally, it depicts relevant challenges that researchers aim to overcome to implement proper IoT-based security solutions. Thus, this survey aims to contribute to the literature by investigating the application of AML concepts within the IoT context. An extensive review of the current research trends of AML within IoT networks is presented. A conclusion is reached where several findings are reported including a shortage of defense mechanisms investigations, a lack of tailored IoT-based solutions, and the applicability of the existing mechanisms in both attack and defense scenarios.
Research on Calibration Method of Line-Structured Light Based on Multiple Geometric Constraints

Line-structured light calibration is an important step in the process of the visual measurement of structured light, and in the calibration process, the light plane equation is fitted by the camera coordinates of the data points on the light plane. There is error in solving the spatial coordinates of the data points, and the existing methods generally cannot effectively find the deviation points in the fitted data points, which leads to deviation when solving the parameters of the light plane equation. To correct the error caused by the deviation point in the calibration, this paper established a calibration method based on geometric constraints. In the method, the first constraint was that all fit points involved in the fitting had a certain consistency, and a part of the error points could be filtered by the distance from the data point to the light plane. In order to further improve the calibration accuracy, the laser was moved on a linear guide, the optimized objective function was established by the second geometric constraint, and the constraint was the relationship that the light plane at each position was parallel. In the experiment, a high-precision gauge block was used as the measured object, and the measurement results could be obtained. The calibration method can improve the calibration accuracy according to the experimental results.
Prospectivity Mapping of Tungsten Mineralization in Southern Jiangxi Province Using Few-Shot Learning

The development of mineral prospectivity mapping (MPM), which aims to outline and prioritize mineral exploration targets, has been spurred by advances in data-driven machine learning algorithms. Supervised data-driven MPM is a typical few-shot task, suffering from a scarcity of labeled data, the over-fitting of models and an uncertainty of predictions. The main objective of this contribution is to propose a robust framework of few-shot learning (FSL), combining data augmentation and transfer learning to enable the generation of prospectivity models with excellent predictive efficiency and low uncertainty. The mineral systems approach was used to transfer a conceptual mineral system into mappable exploration criteria. Synthetic minority over-sampling technique (SMOTE) was employed to augment and balance the labeled dataset, allowing for model pre-training with the large synthetic training dataset of a source domain. The knowledge derived from pre-trained models was then transferred to the target domain by fine-tuning, and the prospectivity model was generated in light of over-fitting and uncertainty assessments. The proposed FSL framework was applied to tungsten prospectivity mapping in southern Jiangxi Province. The results indicated that the SMOTE-ed balanced dataset boosted the classification accuracy in the training process. The FSL models yielded an arch-shaped prediction point pattern which was favorable for focusing potential targets with high probability and low uncertainty. The FSL models achieved a high predictive performance (test AUC = 0.9172) and the lowest quantitative over-fitting value compared to the models derived from the benchmark algorithms of random forest and support vector machine. Four levels of potential targeting zones, considering both predictive efficiency and uncertainty, were extracted from the resulting FSL prospectivity map. The final high-potential and low-risk exploration targets only cover 4.27% of the area, but capture 41.53% of known tungsten deposits, thus achieving a superior predictive performance. This study highlights the capability of FSL framework to control over-fitting and generate high-confidence exploration targets with low levels of uncertainty.
The Modal Logic of Aristotelian Diagrams

In this paper, we introduce and study AD-logic, i.e., a system of (hybrid) modal logic that can be used to reason about Aristotelian diagrams. The language of AD-logic, LAD, is interpreted on a kind of birelational Kripke frames, which we call “AD-frames”. We establish a sound and strongly complete axiomatization for AD-logic, and prove that there exists a bijection between finite Aristotelian diagrams (up to Aristotelian isomorphism) and finite AD-frames (up to modal isomorphism). We then show how AD-logic can express several major insights about Aristotelian diagrams; for example, for every well-known Aristotelian family A, we exhibit a formula χA∈LAD and show that an Aristotelian diagram D belongs to the family A iff χA is validated by D (when the latter is viewed as an AD-frame). Finally, we show that AD-logic itself gives rise to new and interesting Aristotelian diagrams, and we reflect on their profoundly peculiar status.
Research on the Improvement of LSB-based Image Steganography Algorithm

This study has made an improvement based on the LSB algorithm, which uses a randomly generated sequence for embedding information, with an implementation environment of Python and a combination of OpenCV By analyzing the embedded data with PSNR values and pretzel-plus-noise attacks, the results show that this method has better image quality and stronger resistance to attacks, while the complexity of extracting the embedded information by external users is high. The improved LSB algorithm in this study has great reference value for information covert transmission, and can be used for image copyright protection and privacy information protection in practical applications.
Backdoor Attack on Deep Neural Networks Triggered by Fault Injection Attack on Image Sensor Interface

A backdoor attack is a type of attack method that induces deep neural network (DNN) misclassification. The adversary who aims to trigger the backdoor attack inputs the image with a specific pattern (the adversarial mark) into the DNN model (backdoor model). In general, the adversary mark is created on the physical object input to an image by capturing a photo. With this conventional method, the success of the backdoor attack is not stable because the size and position change depending on the shooting environment. So far, we have proposed a method of creating an adversarial mark for triggering backdoor attacks by means of a fault injection attack on the mobile industry processor interface (MIPI), which is the image sensor interface. We propose the image tampering model, with which the adversarial mark can be generated in the actual fault injection to create the adversarial mark pattern. Then, the backdoor model was trained with poison data images, which the proposed simulation model created. We conducted a backdoor attack experiment using a backdoor model trained on a dataset containing 5% poison data. The clean data accuracy in normal operation was 91%; nevertheless, the attack success rate with fault injection was 83%.
Asymmetric bi-directional teleportation scheme in the presence of a Mentor and a controller

In this paper a quantum communication protocol is proposed for the purpose of the exchange of a single qubit and a three-qubit state between two parties. It is a bi-directional teleportation protocol where the two parties have no information about the states they possess. The protocol has a Mentor who initiates the process and by measurement fixes the course of it amongst several possible alternatives. The participants are not initially connected through a single entangled state. It is the action of the Mentor which creates a seven-qubit entanglement amongst the legitimate parties. The role of the Mentor ends thereby. There is a Controller to oversee the process whose action leads to the completion of the protocol. The efficiency of the present protocol is calculated and it is found to be higher than several existing bi-directional protocols.
Smart Iterative Analysis Tool for the Size Distribution of Spherical Nanoparticles

The size of nanoparticles is a critical parameter with regard to their performance. Therefore, precise measurement of the size distribution is often required. While electron microscopy (EM) is a useful tool to image large numbers of particles at once, manual analysis of individual particles in EM images is a time-consuming and labor-intensive task. Therefore, reliable automatic detection methods have long been desired. This paper introduces a novel automatic particle analysis software package based on the circular Hough transform (CHT). Our software package includes novel features to enhance precise particle analysis capabilities. We applied the CHT algorithm in an iterative workflow, which ensures optimal detection over wide radius intervals, to deal with overlapping particles. In addition, smart intensity criteria were implemented to resolve common difficult cases that lead to false particle detection. Implementing these criteria enabled an effective and precise analysis by minimizing detection of false particles. Overall, our approach showed reliable particle analysis results by resolving common types of particle overlaps and deformation with only negligible errors.