Reviewerly/exHarmony huggingface dataset:
Application access for chromatic color detection

Abstract The process of identifying specific examples of an item inside an image or video is referred to as “object detection,” and it is one of the computer vision techniques. When individuals look at pictures or movies, they are able to recognize and find things that are of interest to them. The primary goal of the field of object recognition is to achieve this level of intelligence via the use of a computer. Image segmentation and blob analysis, which uses simple object properties such as size, shape, or color, and Feature-based object detection, which uses feature extraction, matching, and RANSAC to estimate the location of an object, are two of the many common techniques that are used in conjunction with deep learning and machine learning-based object detection. There are also many other common techniques that are used in conjunction with deep learning and machine learning-based object detection. The most challenging aspects of object detection are the processes of categorizing things and pinpointing their locations. When seen from a different perspective, the same object might seem to be completely different. Detection algorithms have a tough time distinguishing between various things at different scales and perspectives since the sizes and proportions of items almost always alter. The camera input would be used to determine an object’s shape, according to this project’s proposal. Circle, square, and triangle are the three distinct shapes that we are determining an item to be right now. The finished result is shown in MATLAB’s graphical user interface (GUI). The productive identification process is carried out with the assistance of MATLAB. Using the SFTA Algorithm, the application extracts features from the specific image of a video. The neural network algorithm is used to classify the image of video. Image segmentation and image detection are two examples of related computer vision methods that are inextricably intertwined with object recognition. These techniques allow us to better comprehend and analyze the situations shown in movies and still photographs. It has been determined whether or not the suggested system is suitable. Because of this, the testing has shown that the suggested system generates generally accurate indications of real performance at a rate of 99.4%.
Weather forecast and its visualization

Abstract We have built a weather app take the current location using latitude &amp; longitude and sends this information to the API next it gets the temperature of popular areas along with the latitude &amp; longitude of them, then recommends the coolest area near user and shows the temperature of the user location. To get the location of the user we have used location listener. The current location of the user is fetched through location listener and data is arranged into an array list. Async Task is a method which is used to access the data at any time required and the task is ran continuously in back ground. A Weather API is used to fetch the current temperature of the popular areas within the radius of 150 km then the data is stored into an array list. From the array lists the data is assigned by a temporary variable to get the minimum temperature and the area. The outcome of the app is in format of suggestion of area with minimum temperature and current location temperature.
Frequency Analysis Attack on Ceaser Cipher using Quantum Support Vector Machine

Quantum technology accelerated computing may have the capacity to provide solutions that are much better compared to their best known classical solutions. Quantum Algorithms reducing the complexity of problems like factorisation and searching of unstructured data has triggered a panic mode among cryptographers to analyse the security state of current cryptography schemes. The paper proposes applying Quantum enhanced State Vector Machine to the Frequency Analysis Attack on Ceaser Cipher, the simplest substitution cipher. The method is message length agnostic as it analyzes the frequency of characters and improves in accuracy with increasing message length. Previous attempts at applying QSVM were focused on using the entire ciphertext and required twice as many qubits as the length of plaintext. Our method uses a quantum circuit only as the kernel for the SVM method and can be implemented with only as many classifiers as the size of the alphabet being used in the ciphertext, irrespective of message size.
Visual Quality Improvement of Watermarked Image Based on Singular Value Decomposition Using Walsh Hadamard Transform

Abstract Embedding the watermark is still a challenge in image watermarking. The watermark should not reduce the visual quality of the image being watermarked and hard to distinguish from its original. Embedding a watermark of a small size might be a good solution. However, the watermark might be easy to lose if there is any tampering with the watermarked image. This research proposes to increase the visual quality of the watermarked image using the Walsh Hadamard transform, which is applied to the singular value decomposition-based image watermarking. Technically, the watermark image is converted into a low bit-rate signal before being embedded in the host image. Using various watermark sizes, experimental results show that the proposed method could produce a good imperceptibility with 47.10 dB on average and also gives robustness close to the original watermark with a normalized correlation close to 1 on average. The proposed method can also recognize the original watermark from the tampered watermarked image at different levels of robustness.
Self-Driving Cars: Simulation, Issues and Possible Solutions for Implementation in India

Self-driving cars being the new technology are gaining attention from all over the globe. This paper deals with the simulation results of an autonomous car trying to learn from its environment which includes static blocks using machine learning. Learning is performed using Deep Q-learning. The neural network computes the Q-values on the basis of the rewards corresponding to the action that the car takes. The autonomous system in the car chooses that particular action that has a maximum reward. The actions are the angles through which the car can steer at a fixed speed. Also, difficulties related to the implementation of autonomous self-driving cars in India have been discussed and possible solutions to them have been presented.
A Scenario Reduction Method of PV Output Based on NBSO Algorithm and STDI Index

Abstract Photovoltaic output has strong randomness and volatility. Research on the scenario reduction method of PV output is of great significance for the planning, design, and operation of power grids. This paper proposes Nonlinear Brain Storm Optimizer (NBSO) to improve the k-means algorithm, and uses the Standard Deviation Based Index (STDI) to evaluate the effectiveness of clustering. The simulation results show that the clustering method proposed in this paper can effectively reduce the PV output scenes, and the generated typical scene set can fit the output volatility and diversity of the initial scenes.
Monitoring PV Output Power in Tropical Climates Under Surface Temperature Distribution Area

Abstract The performance parameters of a solar panel are influenced by many factors, including the type of panel, area, solar radiation, ambient temperature, and weather. This study aims to analyse the effect of solar radiation and temperature on the performance of monocrystalline solar panels. This research was located in Palu, one of the cities with high temperatures in tropical Indonesia. The study used 50 Wp PV, which was placed in the eastern Palu area based on the spatial distribution of surface temperature with the highest temperature distribution. Data sampling is 25 times per day from 11:00 am – 03:00 pm. The result is that increased surface temperature of the solar panel impacts the decrease in the output power of the PV. The environmental temperature of 32.87 °C in the East Palu area causes a decrease of PV power by 2.3%.
Local Corner and Motion Key Point Trajectory Extraction for Facial Forgery Identification

At present, the development of deep forgery technology has brought new challenges to media content forensics, and the use of deep forgery identification methods to identify forged audio and video has become a significant focus of research and difficulty. Deep forgery technology and forensic technology play a mutual game and promote each other’s development. This paper proposes a spatiotemporal local feature abstraction (STLFA) framework for facial forgery identification to solve the media industry challenges of deep forgery technology. To adequately utilize local facial features, we combine facial key points, key point movement, and facial corner points to detect forgery content. This paper establishes a spatiotemporal relation, which realizes face forgery detection by identifying abnormalities of facial keypoints and corner points for interframe judgments. Meanwhile, we utilize RNNs to predict the sequences from facial key point movement abnormalities and corner points for interframe. Experimental results show that our method achieves better performance than some existing methods and good anticompression forgery face detection performance on FF++.
Analysis of Palu City’s Readiness Level Assessment Towards Adipura (case study: Siranindi Village)

Abstract The utilisation of natural energy not only can reduce energy consumption in buildings but also can lower carbon emissions from the use of fossil fuel energy for building services. It is believed that with appropriate design and a good attitude of building users, the role of renewable energy in reducing carbon emissions will be maximised. This study proposed the implementation of Zero Energy Buildings (ZEB) concept in the initial stage of building design by considering buildings’ form and façade design in relation to daylighting, natural ventilation and thermal design of buildings, and photovoltaic placement to save energy and to produce electrical energy in the designed building. In this study, ZEB Concept is treated as a secondary consideration in producing the architectural design for Biohydrogen Research Centre. The primary design generator is based on the philosophy of chemical bonds form representing biohydrogen chemical bonds. For a successful implementation of the ZEB concept in the Biohydrogen Research Centre design, both active and passive means are utilised in the building design. For passive means, daylighting and natural ventilation strategies were applied. While for active means, photovoltaic panels were employed as the primary electrical energy generation. Energy demand scenarios were predicted and calculated by the amount of energy used for lighting, air conditioning, and other appliances in the building. The total area needed for photovoltaic installation was obtained by balancing the energy demand prediction with the expected energy generation. The resulting design showed a promising outcome where the building is expected to achieve surplus energy with a total of 845,595.5 kWh electricity per year.
Monocular 3D Target Detection Model Based on Differential Neural Network Architecture Search

Abstract This paper explores the possibility of applying neural network architecture search to monocular 3D target detection tasks, so as to solve the problem that researchers need a lot of time and prior knowledge to manually design network structures. The algorithm applies the differentiable neural network architecture search technology to the backbones search of the 3D monocular target detection network FCOS3D. At the same time, to improve the accuracy of the algorithm for 3D target detection tasks and reduce the computational complexity of the algorithm, we add deformable convolution and depth separable convolution to the network searchable space. Finally, our algorithm is superior to the original FCOS3D algorithm in the KITTI3D monocular target detection dataset.
Heterogeneous graph neural network with semantic-aware differential privacy guarantees

Most social networks can be modeled as heterogeneous graphs. Recently, advanced graph learning methods exploit the rich node properties and topological relationships for downstream tasks. That means that more private information is embedded in the representation. However, the existing privacy-preserving methods only focus on protecting the single type of node attributes or relationships, which neglect the significance of high-order semantic information. To address this issue, we propose a novel Heterogeneous graph neural network with Semantic-aware Differential privacy Guarantees named HeteSDG, which provides a double privacy guarantee and performance trade-off in terms of both graph features and topology. In particular, we first reveal the privacy leakage in heterogeneous graphs and define a membership inference attack with a semantic enhancement (MIS) that will improve the means of member inference attacks by obtaining side background knowledge through semantics. Then we design a two-stage mechanism, which includes the feature attention personalized mechanism and the topology gradient perturbation mechanism, where the privacy-preserving technologies are based on differential privacy. These mechanisms will defend against MIS and provide stronger interpretation, but simultaneously bring in noise for representation learning. To better balance the noise perturbation and learning performance, we utilize a bi-level optimization pattern to allocate a suitable privacy budget for the above two modules. Our experiments on four public benchmarks conduct performance experiments, ablation studies, inference attack verification, etc. The results show the privacy protection capability and generalization of HeteSDG.
An Improved YOLOv5s-Based Scheme for Target Detection in a Complex Underwater Environment

At present, sea cucumbers, sea urchins, and other seafood products have become increasingly significant in the seafood aquaculture industry. In traditional fishing operations, divers go underwater for fishing, and the complex underwater environment can cause harm to the divers’ bodies. Therefore, the use of underwater robots for seafood fishing has become a current trend. During the fishing process, underwater fishing robots rely on vision to accurately detect sea cucumbers and sea urchins. In this paper, an algorithm for the target detection of sea cucumbers and sea urchins in complex underwater environments is proposed based on the improved YOLOv5s. The following improvements are mainly carried out in YOLOv5s: (1) To enhance the feature extraction ability of the model, the gnConv-based self-attentive sublayer HorBlock module is proposed to be added to the backbone network. (2) To obtain the optimal hyperparameters of the model for underwater datasets, hyperparameter evolution based on the genetic algorithm is proposed. (3) The underwater dataset is extended using offline data augmentation. The dataset used in the experiment is created in a real underwater environment. The total number of created datasets is 1536, and the training, validation, and test sets are randomly divided according to the ratio of 7:2:1. The divided dataset is input to the improved YOLOv5s network for training. The experiment shows that the mean average precision (mAP) of the algorithm is 94%, and the mAP of the improved YOLOv5s model rises by 4.5% compared to the original YOLOv5s. The detection speed increases by 4.09 ms, which is in the acceptable range compared to the accuracy improvement. Therefore, the improved YOLOv5s has better detection accuracy and speed in complex underwater environments, and can provide theoretical support for the underwater operations of underwater fishing robots.
Retrospective-Based Deep Q-Learning Method for Autonomous Pathfinding in Three-Dimensional Curved Surface Terrain

Path planning in complex environments remains a challenging task for unmanned vehicles. In this paper, we propose a decoupled path-planning algorithm with the help of a deep reinforcement learning algorithm that separates the evaluation of paths from the planning algorithm to facilitate unmanned vehicles in real-time consideration of environmental factors. We use a 3D surface map to represent the path cost, where the elevation information represents the integrated cost. The peaks function simulates the path cost, which is processed and used as the algorithm’s input. Furthermore, we improved the double deep Q-learning algorithm (DDQL), called retrospective-double DDQL (R-DDQL), to improve the algorithm’s performance. R-DDQL utilizes global information and incorporates a retrospective mechanism that employs fuzzy logic to evaluate the quality of selected actions and identify better states for inclusion in the memory. Our simulation studies show that the proposed R-DDQL algorithm has better training speed and stability compared to the deep Q-learning algorithm and double deep Q-learning algorithm. We demonstrate the effectiveness of the R-DDQL algorithm under both static and dynamic tasks.
Extreme Sample Imbalance Classification Model Based on Sample Skewness Self-Adaptation

This paper aims to solve the asymmetric problem of sample classification recognition in extreme class imbalance. Inspired by Krawczyk (2016)’s improvement direction of extreme sample imbalance classification, this paper adopts the AdaBoost model framework to optimize the sample weight update function in each iteration. This weight update not only takes into account the sampling weights of misclassified samples, but also pays more attention to the classification effect of misclassified minority sample classes. Thus, it makes the model more adaptable to imbalanced sample class distribution and the situation of extreme imbalance and make the weight adjustment in hard classification samples more adaptive as well as to generate a symmetry between the minority and majority samples in the imbalanced datasets by adjusting class distribution of the datasets. Based on this, the imbalance boosting model, the Imbalance AdaBoost (ImAdaBoost) model is constructed. In the experimental design stage, ImAdaBoost model is compared with the original model and the mainstream imbalance classification model based on imbalanced datasets with different ratio, including extreme imbalanced dataset. The results show that the ImAdaBoost model has good minority class recognition recall ability in the weakly extreme and general class imbalance sets. In addition, the average recall rate of minority class of the mainstream imbalance classification models is 7% lower than that of ImAdaBoost model in the weakly extreme imbalance set. The ImAdaBoost model ensures that the recall rate of the minority class is at the middle level of the comparison model, and the F1-score comprehensive index performs well, demonstrating the strong stability of the minority class classification in extreme imbalanced dataset.
Accelerating Polynomial Evaluation for Integer-wise Homomorphic Comparison and Division

Fully homomorphic encryption (FHE) is a promising tool for privacy-preserving applications, and it enables us to perform homomorphic addition and multiplication on FHE ciphertexts without decrypting them. FHE has two types: one supporting the exact computation and the other supporting the approximate computation. Further the FHE schemes supporting the exact computation have two types, bit-wise FHE, which encrypts a plaintext bit by bit, and integer-wise FHE, which encrypts a plaintext as an integer. Both types of FHE are important depending on the types of computation we need to execute securely. In this work, we focus on integer-wise FHE, and propose improved methods for integer-wise homomorphic comparison and division operations. For a comparison operation, we improve on the work of Iliashenko and Zucca (PoPETs'21) whose complexity is O(p) homomorphic multiplications, and achieve the complexity $O(\!\!\sqrt{p})$ where p is a plaintext modulus. For a division operation, as opposed to the work of Okada et al. (WISTP'18), we propose a simple method to reduce the processing time by introducing an equality function based on Fermat's little theorem without changing the multiplicative depth, and show the analysis of why this approach can achieve better efficiency in detail. In our homomorphic division, the number of interpolated polynomials is reduced by half, thus also achieving the reduction of the processing time of precomputations and the number of polynomials to be stored. We also implement our improved methods in HElib, which is one of popular FHE libraries using the BGV encryption. As a result, we show that, e.g., in the plaintext space ℤ257, our homomorphic comparison with the Paterson-Stockmeyer method is faster by a factor of about 14.5 compared with Iliashenko and Zucca (PoPETs'21) and our homomorphic division is faster by a factor of about 1.45 compared with Okada et al. (WISTP'18).
The Impact of Control of Corruption, Human Development Index, and Macroeconomics on Economic Growth Rates in Low-Middle Income Countries

Economic growth is part of the indicators used in assessing economic performance and it also becomes a benchmark for developing a country. Therefore, this study aims to determine the effect of control of corruption, human development index, inflation, and exchange rate on economic growth in 15 low-middle-income countries in Asia between 2016–2020. Furthermore, secondary data obtained from the World Bank in the form of panel data were utilized and processed using the EViews 10 analysis tool. The results showed control of corruption and the human development index had a positive and significant impact on the level of economic growth in Asia's lower middle-income countries in 2016-2020. However, inflation and exchange rates had a negative and significant impact on economic growth rates.
Data Search Process Optimization using Brute Force and Genetic Algorithm Hybrid Method

High accuracy and speed in data search, which are aims at finding the best solution to a problem, are essential. This study examines the brute force method, genetic algorithm, and two proposed algorithms which are the development of the brute force algorithm and genetic algorithm, namely Multiple Crossover Genetic, and Genetics with increments values. Brute force is a method with a direct approach to solving a problem based on the formulation of the problem and the definition of the concepts involved. A genetic algorithm is a search algorithm that uses genetic evolution that occurs in living things as its basis. This research selected the case of determining the pin series by looking for a match between the target and the search result. To test the suitability of the method, 100-time tests were conducted for each algorithm. The results of this study indicated that brute force has the highest average generation rate of 737146.3469 and an average time of 1960.4296, and the latter algorithm gets the best score with an average generation rate of 36.78 and an average time of 0.0642.
A Study on Iconic Paper Discovery Based on Deep Learning Models

This paper proposes a new method for selecting iconic papers. We use chapter structure recognition technology and combine it with popular deep learning models to identify the chapter structure of the papers. Based on this identification result, we calculate the sum of the number of times each paper is mentioned in all articles, to discover iconic papers with high mention frequency. We focus on the "Method" and "Conclusion" sections to find papers that are frequently mentioned in these two sections and determine iconic papers. With this method, we can more accurately discover and evaluate important research results and provide more valuable references for scholars in related fields.