Reviewerly/exHarmony huggingface dataset:
Enhancing the feature selection by employing improved optimization with Simulated Annealing Algorithm for dimensionality reduction in intrusion detection dataset

In the current scenario, feature selection (FS) remains one of the very important functions in machine learning. Decreasing the feature set (FSt) assists in enhancing the classifier’s accuracy. Because of the existence of a huge quantity of data within the dataset (DS), it remains a colossal procedure for choosing the requisite features out of the DS. Hence, for resolving this issue, a new Chaos Quasi-Oppositional-based Flamingo Search Algorithm with Simulated Annealing Algorithm (CQOFSASAA) has been proffered for FS and for choosing the optimum FSt out of the DSs, and, hence, this lessens the DS’ dimension. The FSA technique can be employed for selecting the optimal feature subset out of the DS. Generalized Ring Crossover has been as well embraced for selecting the very pertinent features out of the DS. Lastly, the Kernel Extreme Learning Machine (KELM) classifier authenticates the chosen features. This proffered paradigm’s execution has been tested by standard DSs and the results have been correlated with the rest of the paradigms. From the experimental results, it has been confirmed that this proffered CQOFSASAA attains 93.74% of accuracy, 92% of sensitivity, and 92.1% of specificity.
MonoGhost: Lightweight Monocular GhostNet 3D Object Properties Estimation for Autonomous Driving

Effective environmental perception is critical for autonomous driving; thus, the perception system requires collecting 3D information of the surrounding objects, such as their dimensions, locations, and orientation in space. Recently, deep learning has been widely used in perception systems that convert image features from a camera into semantic information. This paper presents the MonoGhost network, a lightweight Monocular GhostNet deep learning technique for full 3D object properties estimation from a single frame monocular image. Unlike other techniques, the proposed MonoGhost network first estimates relatively reliable 3D object properties depending on efficient feature extractor. The proposed MonoGhost network estimates the orientation of the 3D object as well as the 3D dimensions of that object, resulting in reasonably small errors in the dimensions estimations versus other networks. These estimations, combined with the translation projection constraints imposed by the 2D detection coordinates, allow for the prediction of a robust and dependable Bird’s Eye View bounding box. The experimental outcomes prove that the proposed MonoGhost network performs better than other state-of-the-art networks in the Bird’s Eye View of the KITTI dataset benchmark by scoring 16.73% on the moderate class and 15.01% on the hard class while preserving real-time requirements.
Editorial on the Special Issue: New Trends in Image Processing III

The image processing field is undergoing a significant transformation owing to rapid advancements in deep learning, computer vision, and artificial intelligence [...]
Traffic image dehazing based on sky region segmentation and transmittance optimization

Traffic sign recognition is of great significance to promote traffic sustainability and maintain traffic safety. GPS monitoring systems and advanced autonomous vehicles are often heavily reliant on camera imagery. Algorithms based on dark channel prior are susceptible to color distortion when processing traffic images containing bright sky or high-brightness areas, which can negatively impact the identification of traffic signals and signage located in elevated positions. To address this issue, this paper proposes a dehazing algorithm (SRSTO) that combines sky region segmentation and transmittance optimization. Firstly, the gradient, brightness and saturation information are calculated, followed by the construction of a threshold function used in area segmentation. This approach is utilized to partition the image into areas not containing sky highlights and the area that contains them. Subsequently, the dark channel images of the sky and the non-sky regions are acquired, morphological operations are further performed in layers and blocks, and then the atmospheric scattered light value is calculated. Secondly, the functional relationship between the transmittance of the sky region and the brightness of the image is constructed, the transmittance of the sky and the non-sky region are optimized, and the transmittance map is further improved by using guided filtering. A simulated annealing algorithm is employed to intelligently optimize parameters such as sky segmentation threshold and sky brightness area transmittance, followed by improving the adaptability of the algorithm. Finally, combined with Gaussian filtering and Sobel edge enhancement, the image brightness is further adjusted. Using Information Entropy and NIQE as objective evaluation indexes, combined with subjective evaluation, it is concluded that the proposed method has good convergence and self-adaptive ability, and the objective indexes and subjective effects are better, especially for the hazed images containing air traffic signs.
Justification, stability and relevance in incomplete argumentation frameworks

We explore the computational complexity of justification, stability and relevance in incomplete argumentation frameworks (IAFs). IAFs are abstract argumentation frameworks that encode qualitative uncertainty by distinguishing between certain and uncertain arguments and attacks. These IAFs can be completed by deciding for each uncertain argument or attack whether it is present or absent. Such a completion is an abstract argumentation framework, for which it can be decided which arguments are acceptable under a given semantics. The justification status of an argument in a completion then expresses whether the argument is accepted (in), not accepted because it is attacked by an accepted argument (out) or neither (undec). For a given IAF and certain argument, the justification status of that argument need not be the same in all completions. This is the issue of stability, where an argument is stable if its justification status is the same in all completions. For arguments that are not stable in an IAF, the relevance problem is of interest: which uncertain arguments or attacks should be investigated for the argument to become stable? In this paper, we define justification, stability and relevance for IAFs and provide a complexity analysis for these problems under grounded, complete, preferred and stable semantics.
Wavelet-domain human activity recognition utilizing convolutional neural networks

Human activity recognition (HAR) is a crucial area of research in human-computer interaction. Despite previous efforts in this field, there is still a need for more accurate and robust methods that can handle time-series data from different sensors. In this study, we propose a novel method that gen erates an image using wavelet transform to extract time-frequency features of the recorded signal. Our method employs convolutional neural networks (CNNs) for feature extraction and activity recognition, and a new loss function that produces denser representations for samples, improving the model’s generalization on unseen samples. To evaluate the effectiveness of our proposed method, we conducted experiments on multiple publicly available data sets. Our results demonstrate that our method outperforms previous methods in terms of activity classification accuracy. Specifically, our method achieves higher accuracy rates and demonstrates improved robustness in real-world settings. Overall, our proposed method addresses the research gap of accurate and robust activity recognition from time-series data recorded from different sensors. Our findings have the potential to improve the accuracy and robustness of human activity recognition systems in real-world applications.
A Comparative Study of BFV and CKKs Schemes to Secure IoT Data Using TenSeal and Pyfhel Homomorphic Encryption Libraries

Internet of things (IoT) devices and applications are on the rise, generating large amounts of sensitive and confidential data that need to be processed securely. Due to resource constraints, the data generated is often stored and processed in the cloud. The drawback of data cloud storage and processing is the fact that it can be hacked, leaked, or sold by cloud companies. Fully homomorphic encryption (FHE) allows computation on encrypted data using basic mathematical operations and has recently been successfully implemented using schemes and libraries with better performance. In this paper, the authors propose a mixture of edge-cloud-based security schemes using FHE to secure IoT data. The authors evaluate the performance of two FHE schemes (BFV and CKKS) based on data: encoding speed, encryption speed, arithmetic operations (addition and multiplication) speed, and decryption decoding speed using two Python libraries (TenSEAL and PyFHEl). The encryption and decryption are done at the edge node using a Raspberry Pi 4, while the processing is done at the cloud node using a laptop.
Analysis of neural network detectors for network attacks

While network attacks play a critical role in many advanced persistent threat (APT) campaigns, an arms race exists between the network defenders and the adversary: to make APT campaigns stealthy, the adversary is strongly motivated to evade the detection system. However, new studies have shown that neural network is likely a game-changer in the arms race: neural network could be applied to achieve accurate, signature-free, and low-false-alarm-rate detection. In this work, we investigate whether the adversary could fight back during the next phase of the arms race. In particular, noticing that none of the existing adversarial example generation methods could generate malicious packets (and sessions) that can simultaneously compromise the target machine and evade the neural network detection model, we propose a novel attack method to achieve this goal. We have designed and implemented the new attack. We have also used Address Resolution Protocol (ARP) Poisoning and Domain Name System (DNS) Cache Poisoning as the case study to demonstrate the effectiveness of the proposed attack.
Approximation Algorithms for LCS and LIS with Truly Improved Running Times

Longest common subsequence (LCS) is a classic and central problem in combinatorial optimization. While LCS admits a quadratic time solution, recent evidence suggests that solving the problem may be impossible in truly subquadratic time. A special case of LCS wherein each character appears at most once in every string is equivalent to the longest increasing subsequence (LIS) problem which can be solved in quasilinear time. In this work, we present novel algorithms for approximating LCS in truly subquadratic time and LIS in truly sublinear time. Our approximation factors depend on the ratio of the optimal solution size to the input size. We denote this ratio by and obtain the following results for LCS and LIS without any prior knowledge of : a truly subquadratic time algorithm for LCS with approximation factor and a truly sublinear time algorithm for LIS with approximation factor . The triangle inequality was recently used by M. Boroujeni, S. Ehsani, M. Ghodsi, M. HajiAghayi, and S. Seddingham [Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms, 2018, pp. 1170–1189] and D. Chakraborty, D. Das, E. Goldenberg, M. Koucky, and M. Saks [Proceedings of the 59th Annual IEEE Symposium on Foundations of Computer Science, 2018, pp. 979–990] to present new approximation algorithms for edit distance. Our techniques for LCS extend the notion of the triangle inequality to nonmetric settings.
Bitcoin double-spending risk and countermeasures at physical retail locations

This research examines vulnerabilities and potential attacks using Bitcoin payment systems at physical retail locations. It also lays a theoretical and practical foundation for effective countermeasures that ensure the integrity of decentralized transactions and bolster the confidence of both merchants and customers. The study focuses on potential double-spend attacks, particularly in the context of instant payments made via non-custodial crypto wallets. It also evaluates alternatives for instant crypto payments that use alternative coins and second-layer blockchain solutions. Research has indicated that Eclipse, Sybil, Majority, Race, and Finney attacks carry a relatively low risk, especially when payment amounts are limited. The paper identifies Replace-By-Fee attacks as the most significant threat and presents a set of countermeasures. These findings are of great importance to cryptocurrency experts, payment tech researchers, retail enterprises, and stakeholders using non-custodial wallets while exploring decentralized blockchain aspects.
Smart contracts and homomorphic encryption for private P2P energy trading and demand response on blockchain

Blockchain technology offers great value in terms of decentralization, data integrity, transparency, and traceability, however the transactional data is public, and accessible raising concerns about violating privacy regulations. For example, in the peer-to-peer energy trading and demand response use cases, the data stored in blockchain may allow a third party to infer the load profiles or even identify the behind the meter assets. In this paper, we employ homomorphic techniques to encrypt the energy transactional data stored on the blockchain allowing the smart contracts functions responsible for implementing the business logic of the energy flexibility trading and settlement to perform computations on encrypted data. As computations on smart contracts and public blockchains can be expensive, we have used the lighter version of the Partial Homomorphic Encryption scheme to obfuscate the energy data. To ensure the validity of the smart contracts' functions executed on encrypted data, we leverage on the consensus mechanism of the blockchain network, thus ensuring computation correctness. The solution was validated considering a micro-grid with 12 prosumers that trade their flexibility peer-to-peer (P2P). The results demonstrate the feasibility of maintaining encrypted energy data on the blockchain, executing smart contract functions on encrypted data, and preserving the privacy of computations. As anticipated, the trade-off for better privacy is the gas consumption overhead of the smart contracts' functions which is higher compared to the non-encrypted case, depending on the length of the public-private keys pair. Nonetheless, our solution exhibits consistent execution times for smart contracts, making it suitable for private networks where gas costs are of minimal concern.
Enhanced Semantic Matching with Topic-aware and Fine-grained User Modeling for News Recommendation

Online news articles contain representative textual content including title, abstract, category and entities, and the clicked news articles by users indicate their interests. Fully exploiting these features is critical for accurate matching between candidate news and user interests. In this paper, we propose a topic-aware news encoder to enhance rich textual representations of the news. Moreover, we propose a unified semantic matching framework to model fine-grained user interests and user-news matching by explicitly modeling both high-order feature interactions between a candidate news and user’s news history, and their temporal dependency, which are integrated into a unified transformer encoder with considering the news semantic representation, the type and the position embeddings as the input embeddings. Then we introduce a single-layer neural network based on the interest matching for the user click-through rate prediction. Extensive experiments on the benchmark dataset MIND show that it can effectively improve the performance of news recommendation.
Preoperative MR Image prediction of breast cancer based on self-supervised learning

Breast cancer is a prevalent cancer type among women worldwide, which contributes to significant mortality and morbidity rates. Early detection of breast cancer plays a crucial role in improving patients’ chances of survival. In this study, we utilized a publicly available dataset consisting of Magnetic Resonance (MR) images from 1,480 individuals. Leveraging deep learning technology, we successfully developed an accurate automatic detection system for breast cancer in MR images. To address the challenge of limited medical datasets, we propose a model based on self-supervised learning. This approach allows us to effectively overcome the limitations associated with small-scale datasets. Our experimental results demonstrate that our proposed model significantly enhances the predictive performance of breast cancer detection. Notably, our model achieved a remarkable six percentage point improvement in terms of AUC (reaching 0.95), when compared to the baseline model. The outcomes of this research present a promising step towards automated and precise prediction of breast cancer. The application of our model can aid in early-stage breast cancer detection, enabling timely treatment planning and ultimately enhancing patient survival rates.
Two-Stage Feature Attention Fusion for Radar-Camera 3D Object Detection

Multi-sensor fusion is essential for 3D object detection in intelligent transportation due to it makes best use of cross-modality information, in which feature-level fusion of millimeter-wave radar and camera has been a hot topic. Existing research considers only one of the fusion operations when fusing radar and camera features, such as concatenation, element-wise addition, and element-wise multiplication, however, these fusion operations have the potential to be combined, as they are complementary. To fill the gap mentioned above, this paper proposes a two-stage feature attention fusion (TSAF) network for radar-camera 3D object detection, where we introduce multiplication fusion in the first stage of fusion and concatenation fusion in the second stage of fusion. At the same time, a modified radar spatial attention module (RSAM) is exploited in the first stage to improve the utilization of the radar features by reallocating the weights of the radar key information. Moreover, a camera channel attention module (CCAM) is applied in the second stage of fusion to enhance the camera key feature. The proposed fusion network is evaluated on the nuScenes dataset. Experimental results show that, compared to the baseline, TSAF improves the mean average precision (mAP) from 35.3% to 38.8%, and the nuScenes Detection Score (NDS) from 43.4% to 48.8%, which verify the effectiveness of TSAF.
Toward Improving the Prediction Accuracy of a Product Recommendation System Based on Word Sequential Using LSTM Embedded

The ability to predict purchases is crucial for e-commerce decision makers when making offers and suggestions to customers. In the development of recommendation models, two common problems often encountered are a lack of personalization and irrelevant recommendations. To address these issues, it is crucial to consider user history data, such as the user's interactions with previous products. This allows the model to learn user preferences from the past and generate more personalized and relevant recommendations. In this study, word2vec is used to provide rating predictions, while document context is enhanced using LSTM capture contextual understanding of product reviews. This study makes use of an actual dataset to test our model using an Amazon Review Dress. The results of our investigation demonstrate that, as 88% of the recommendations are made in accordance with the recommendation system's criteria, it can be considered that it offers reasonably accurate and dependable recommendations with an accuracy of 0.8752
Study on the Use of Multidimensional Database Systems for Demography Data Analysis

The demography database system should be very useful for various purposes in overcoming the socio-economic problems of the population, such as the distribution of charity, especially for people affected by the Covid-19 pandemic. For example, those who have lost their jobs, those who have difficulty in accessing medical services, etc., or for general election data, for example, presidential election, legislator election, or governor election. But unfortunately, the demography database system in our country, Indonesia still has many problems. Things to consider are that there is no integration between databases at the central and local governments, besides that there is no synchronization of data between institutions. As a result, there are many mistakes in the distribution of charity for people affected by the Covid-19 pandemic in 2020, such as those who should receive do not receive it, and those who should not receive it receive it. This problem can be caused by the absence of a definite reference in the population data. As an example, On the time dimension, one year's data can be multi-interpreted both between the central and the regions government as well as between agencies. This study aims to build an integrated demography database system through a multidimensional database system model. In the multidimensional database system, the data is a cell that can show information from multi-dimensions (such as spatial, temporal, and variable). Each dimension in a multidimensional database system is interconnected in a system so that it can eliminate multiple interpretations of data in one dimension or between dimensions. Each dimension in a multidimensional database system can be arranged in a hierarchy. In the spatial dimension, it can be arranged based on a hierarchy of administrative units, such as from state down to province, then district/city, sub-district, village, and so on. So that it can eliminate the non-integration between data on the central and local governments. In the dimension of variable, it can be arranged based on the level of detail of demography data, and the total population as the top level of detail. While the next level of detail can be arranged based on criteria from demographic data such as sex, age, occupation, education, etc. So that it can eliminate the non-synchronization of data between institutions.
Expert System Diagnose Broiler Chicken Diseases using Case Based Reasoning Method and Sorensen Dice Coefficient

Delays in handling broiler chicken diseases cause a lot of losses which are quite large and even cause death. The limitations of an expert and relatively long veterinarian visits. The purpose of this research was to apply the case-based reasoning method and Sorensen dice coefficient for the application of an expert system in diagnosing broiler chicken diseases. This study used a case-based reasoning method to obtain similar cases to be used in new cases by applying 4 stages include retrieve, reuse, revise, and retain. Sorensen dice coefficient similarity method was used to obtain similarity results. The application of an expert system diagnosed disease using case-based reasoning and Sorensen dice coefficient while obtaining test results on system accuracy of 90% and system reliability of 87%. The results of the accuracy and effectiveness test of the system showed a fairly high value and indicated that the system provided an accurate and effective value compared to previous methods in diagnosing and providing the solutions or treatment.