Reviewerly/exHarmony huggingface dataset:
Small Target Detection for UAV Aerial Images Based on Improved YOLOv3

Aiming at the high rate of missed detection and false detection rate of small targets in UAV aerial photography in target detection, this paper proposes a small target detection method based on improved YOLOv3.Based on the YOLOv3 model, the 8-fold downsampled feature map output from the network is stitched with the 4-fold downsampled feature map to create a new $104 \times104$ scale detection layer. A new feature fusion network, BiFPN, is introduced to enhance feature extraction. The proposed algorithm is tested in simulation experiments on VisDrone2019 dataset, and the experimental results show that the model improves 7% over the base model with almost no impact on speed, and the detection accuracy of small targets is significantly improved.
A privacy preserving identification protocol for smart contracts

If on one hand the possibility of using pseudonymous identities is an important feature of blockchains and smart contracts, on the other hand official identity can be required in some applications to comply with regulations such as Know Your Customer and Anti Money Laundering. These regulatory compliance issues are usually dealt with either through “custo-dial” approaches, which however neutralize the decentralization of these systems, or with “whitelisting” mechanisms, that present critical issues with regard to privacy. In this paper we propose a protocol that allows only potentially identifiable users to use a given decentralized applications. The system ensures that users are identifiable only by a competent authority, under certain conditions, and that in any other case they remain pseudonyms. To evaluate performance and costs, we present an Ethereum implementation of the protocol.
Generating A New Shilling Attack for Recommendation Systems

A collaborative filtering-based recommendation system has been an integral part of e-commerce and e-servicing.To keep the recommendation systems reliable, authentic, and superior, the security of these systems is very crucial.Though the existing shilling attack detection methods in collaborative filtering are able to detect the standard attacks, in this paper, we prove that they fail to detect a new or unknown attack.We develop a new attack model, named Obscure attack, with unknown features and observed that it has been successful in biasing the overall top-N list of the target users as intended.The Obscure attack is able to push target items to the top-N list as well as remove the actual rated items from the list.Our proposed attack is more effective at a smaller number of k in top-k similar user as compared to other existing attacks.The effectivity of the proposed attack model is tested on the MovieLens dataset, where various classifiers like SVM, J48, random forest, and naïve Bayes are utilized.
Identifying and Translating Subjective Content Descriptions Among Texts

An agent pursuing a task may work with a corpus of documents as a reference library. Subjective content descriptions (SCDs) provide additional data that add value in the context of the agent’s task. In the pursuit of documents to add to the corpus, an agent may come across new documents where content text and SCDs from another agent are interleaved and no distinction can be made unless the agent knows the content from somewhere else. Therefore, this paper presents a hidden Markov model-based approach to identify SCDs in a new document where SCDs occur inline among content text. Additionally, we present a dictionary selection approach to identify suitable translations for content text and SCDs based on [Formula: see text]-grams. We end with a case study evaluating both approaches based on simulated and real-world data.
Global stability of fuzzy cognitive maps

Abstract Complex systems can be effectively modelled by fuzzy cognitive maps. Fuzzy cognitive maps (FCMs) are network-based models, where the connections in the network represent causal relations. The conclusion about the system is based on the limit of the iteratively applied updating process. This iteration may or may not reach an equilibrium state (fixed point). Moreover, if the model is globally asymptotically stable, then this fixed point is unique and the iteration converges to this point from every initial state. There are some FCM models, where global stability is the required property, but in many FCM applications, the preferred scenario is not global stability, but multiple fixed points. Global stability bounds are useful in both cases: they may give a hint about which parameter set should be preferred or avoided. In this article, we present novel conditions for the global asymptotical stability of FCMs, i.e. conditions under which the iteration leads to the same point from every initial vector. Furthermore, we show that the results presented here outperform the results known from the current literature.
Kathakali face expression detection using deep learning techniques

Abstract To develop a Deep Learning algorithm that detects the Kathakali face expression (or Navarasas) from a given image of a person who performs Kathakali. One of India’s major classical dance forms is Kathakali. It is a “story play” genre of art, but one distinguished by the traditional male-actor-dancers costumes, face masks and makeup they wear. In the Southern region of India, Kathakali is a Hindu performance art in Malayalam speaking. Most of the plays are epic scenes of Mahabharata and Ramayana. A lot of foreigners visiting India are inspired by this art form and have been curious about the culture. It is still used for entertainment as a part of tourism and temple rituals. An understanding of facial expressions are essential so as to enjoy the play. The scope of the paper is to identify the facial expressions of Kathakali to have a better understanding of the art play. In this paper, Machine Learning and Image Processing techniques are used to decode the expressions. Kathakali face expressions are nine types namely-Adbhutam (wonder), Hasyam (comic), Sringaram(love), Bheebatsam(repulsion), Bhayanakam(fear), Roudram(anger), Veeram(pride), Karunam(sympathy) and Shantham (peace). These Expressions are mapped to real world human emotions for better classification through face detection and extraction to achieve the same. Similarly a lot of research in terms of Preprocessing and Classification is done to achieve the maximum accuracy. Using CNN algorithm 90% of the accuracy was achieved. In order to conserve the pixel distribution and as no preprocessing was used for better object recognition and analysis Fuzzy algorithm is taken into consideration. Using this preprocessing technique 93% accuracy was achieved.
Intelligent Dendritic Neural Model for Classification Problems

In recent years, the dendritic neural model has been widely employed in various fields because of its simple structure and inexpensive cost. Traditional numerical optimization is ineffective for the parameter optimization problem of the dendritic neural model; it is easy to fall into local in the optimization process, resulting in poor performance of the model. This paper proposes an intelligent dendritic neural model firstly, which uses the intelligent optimization algorithm to optimize the model instead of the traditional dendritic neural model with a backpropagation algorithm. The experiment compares the performance of ten representative intelligent optimization algorithms in six classification datasets. The optimal combination of user-defined parameters for the model evaluates by using Taguchi’s method, systemically. The results show that the performance of an intelligent dendritic neural model is significantly better than a traditional dendritic neural model. The intelligent dendritic neural model has small classification errors and high accuracy, which provides an effective approach for the application of dendritic neural model in engineering classification problems. In addition, among ten intelligent optimization algorithms, an evolutionary algorithm called biogeographic optimization algorithm has excellent performance, and can quickly obtain high-quality solutions and excellent convergence speed.
Intra-day global horizontal irradiance forecast using FY-4A clear sky index

Accurate forecasting of Global horizontal irradiance (GHI) plays an irreplaceable role in power system expansion, power generation production scheduling, maintenance scheduling and ensuring continuous power supply. A method for GHI forecasting based on FY-4A satellite images is proposed, called FY-4A-Heliosat. This method is derived from the clear sky model (MCclear) and the future clear sky index (CSI), which is obtained by comparing the continuous cloud albedo (CAL) maps derived from AGRI satellite images according to Heliosat-2 method. The forecast horizon is 30 min to 3 h with a 30-min horizon resolution. The combination of climatology and persistence model (CP) is chosen as reference forecasts. Under all sky conditions, the results show that compared with CP model, FY-4A-Heliosat model is better in most cases and seasons. The FY-4A-Heliosat method underestimates the GHI in four seasons for all time horizons. Annual studies indicate that the FY-4A-Heliosat method performs better than the CP method for all forecast horizons; the nRMSE is 17% to 25% for the annual performance. Forecasts with change in cloud conditions show that: the FY-4A-Heliosat method performs better than the CP method in clear, partially cloudy, overcast and clear-to-cloudy conditions. For the cloudy-to-clear set, the FY-4A-Heliosat method needs to be improved future. The results demonstrate that the proposed method is suitable for very short term GHI forecasting.
Interrogating the performance of quantum annealing for the solution of steady-state subsurface flow

In the 1990s, the principles of quantum mechanics were leveraged to describe how discrete combinatorial optimization problems could be solved using a method called quantum annealing. D-Wave Systems, Inc. has since implemented commercial embodiments. We focus on the use of that hardware to solve discrete hydrologic inverse problems, which can become computationally intense at even moderate sizes. In particular, we interrogate whether contemporary quantum annealers with more qubits, lower noise, and reverse annealing features can improve performance. We find modest improvements in speed and accuracy are possible; however, those benefits are currently constrained to a regime of very rapid, approximate solutions on small meshes. As quantum annealers continue to improve, we expect further encroachment on the performance of classical numerical solvers.
Efficient Forgery Detection Approaches for Digital Color Images

This paper is concerned with a vital topic in image processing: color image forgery detection. The development of computing capabilities has led to a breakthrough in hacking and forgery attacks on signal, image, and data communicated over networks. Hence, there is an urgent need for developing efficient image forgery detection algorithms. Two main types of forgery are considered in this paper: splicing and copy-move. Splicing is performed by inserting a part of an image into another image. On the other hand, copy-move forgery is performed by copying a part of the image into another position in the same image. The proposed approach for splicing detection is based on the assumption that illumination between the original and tampered images is different. To detect the difference between the original and tampered images, the homomorphic transform separates the illumination component from the reflectance component. The illumination histogram derivative is used for detecting the difference in illumination, and hence forgery detection is accomplished. Prior to performing the forgery detection process, some pre-processing techniques, including histogram equalization, histogram matching, high-pass filtering, homomorphic enhancement, and single image super-resolution, are introduced to reinforce the details and changes between the original and embedded sections. The proposed approach for copy-move forgery detection is performed with the Speeded Up Robust Features (SURF) algorithm, which extracts feature points and feature vectors. Searching for the copied partition is accomplished through matching with Euclidian distance and hierarchical clustering. In addition, some pre-processing methods are used with the SURF algorithm, such as histogram equalization and single-mage super-resolution. Simulation results proved the feasibility and the robustness of the pre-processing step in homomorphic detection and SURF detection algorithms for splicing and copy-move forgery detection, respectively.
HammerFilter: Robust Protection and Low Hardware Overhead Method for RowHammer

The continuous scaling-down of the dynamic random access memory (DRAM) manufacturing process has made it possible to improve DRAM density. However, it makes small DRAM cells susceptible to electromagnetic interference between nearby cells. Unless DRAM cells are adequately isolated from each other, the frequent switching access of some cells may lead to unintended bit flips in adjacent cells. This phenomenon is commonly referred to as RowHammer. It is often considered a security issue because unusually frequent accesses to a small set of rows generated by malicious attacks can cause bit flips. Such bit flips may also be caused by general applications. Although several solutions have been proposed, most approaches either incur excessive area overhead or exhibit limited prevention capabilities against maliciously crafted attack patterns. Therefore, the goals of this study are (1) to mitigate RowHammer, even when the number of aggressor rows increases and attack patterns become complicated, and (2) to implement the method with a low area overhead.We propose a robust hardware-based protection method for RowHammer attacks with a low hardware cost called HammerFilter, which employs a modified version of the counting bloom filter. It tracks all attacking rows efficiently by leveraging the fact that the counting bloom filter is a space-efficient data structure, and we add an operation, HALF-DELETE, to mitigate the energy overhead. According to our experimental results, the proposed method can completely prevent bit flips when facing artificially crafted attack patterns (five patterns in our experiments), whereas state-of-the-art probabilistic solutions can only mitigate less than 56% of bit flips on average. Furthermore, the proposed method has a much lower area cost compared to existing counter-based solutions (40.6× better than TWiCe and 2.3× better than Graphene).
Grammar-based compression and its use in symbolic music analysis

We apply Context-free Grammars (CFG) to measure the structural information content of a symbolic music string. CFGs are appropriate to this domain because they highlight hierarchical patterns, and their dictionary of rules can be used for compression. We adapt this approach to estimate the conditional Kolmogorov complexity of a string with a concise CFG of another string. Thus, a related string may be compressed with the production rules for the first string. We then define an information distance between two symbolic music strings, and show that this measure can separate genres, composers and musical styles. Next, we adapt our approach to a model-selection problem, expressing the model as a CFG with restricted size, generated from a set of representative strings. We show that a well-generated CFG for a composer identifies characteristic patterns that can significantly compress other pieces from the same composer, while not being useful on pieces from different composers. We identify further opportunities of this approach, including using CFGs for generating new music in the style of a composer.
Policy-based monocular vision autonomous quadrotor obstacle avoidance method

Abstract Aiming at the obstacle avoidance control problem of small quadrotor, a method of quadrotor obstacle avoidance based on reinforcement learning is proposed. The proposed method can make training converge quickly and has good environmental robustness. The proposed methods include: (1) a framework adopts perception module and decision module to improve the generalization ability of the obstacle avoidance model; (2) An Actor-Critic framework-based Proximal Policy Optimization (PPO) algorithm to provide quadrotor with policy-based decision-making capabilities; The experimental simulation results show that the strategy-based framework converges quickly and has a high success rate, the training time is much lower than that of the value-based framework. The monocular vision observation ability is limited, which leads to deviations between local observation and global state, So LSTM layer is usually added to increase model performance. Policy -based decision can have a good obstacle avoidance effect without adding the LSTM layer, and have good generalization ability after short relearning after changing.
Realizing balanced object detection through prior location scale information and repulsive loss

Object detection is one significant field of computer vision. The imbalance problem exerts negative effects on achieving satisfactory performance. We reveal two sources of imbalance in existing object detection methods. Correspondingly, we propose our methods in terms of the model architecture and optimization target. Different from general object detection benchmarks, the location distribution of objects with different sizes is unbalanced in many practical applications. In addition, the representation information of different categories of objects is unbalanced. In this paper, we propose a location scale equilibrium module to utilize the prior location scale information and generate more balanced feature maps. More appropriate feature maps are selected and merged for different locations. After merging, feature maps become more consistent in terms of representation content, exerting positive effects on the following classification and regression tasks. For the imbalance caused by similar objects, we propose the repulsive loss to strengthen the punishment. Our method will not treat all categories of objects equally since we take the imbalance between them into consideration. With the enhanced supervision, the training will pay more attention to similar objects. Our proposed model is evaluated on the VisDrone benchmark and UAVDT benchmark. Sufficient experiments are conducted. Our model achieves the highest precision on most evaluation metrics, outperforming the other strong models.
A Novel Approach for Initializing Centroid at K-Means Clustering in Paradigm of Computational Geometry

A hybrid k-means method consisting traditional k-means clustering and computational geometry has been presented in this paper. The k-means clustering algorithm strongly depends on the initial cluster centers, and the performance of k-means is sensitive on this initial centroid. The proposed method in this paper shows that instead of random initialization for traditional k-means, if the initial centroids are selected from the circumference points of empty circles, the performance of k-means can be improved significantly. The implementation part of our proposed method and the subsequent valid experimentation with a synthetic data set are done with PYTHON. Our experimented results show its novelty in comparison with traditional k-means.
Segmentation of seagrass (Enhalus acoroides) using deep learning mask R-CNN algorithm

Abstract Seagrass is an Angiosperms that live in shallow marine waters and estuaries. The method commonly used to identify seagrass is Seagrass-Watch which is done by sampling seagrass or by carrying a seagrass identification book. Technological developments in the era of the industrial revolution 4.0 made it possible to identify seagrass automatically. This research aims to apply the deep learning algorithm to detect seagrass recorded by underwater cameras. Enhalus acoroides seagrass species identification was carried out using a deep learning method with the mask region convolutional neural networks (Mask R-CNN) algorithm. The steps in the research procedure include collecting, labeling, training, testing models, and calculating the seagrass area. This study used 6000 epochs and got a measure of value generated by the model of ± 1.2. The Precision value, namely the model’s ability to correctly classify objects, reached 98.19% and the model’s ability to find all positive objects, based on system testing was able to perform recall is 95.04% and the F1 Score value of 96.58%. The results showed that the MASK R-CNN algorithm could detect and segment seagrass Enhalus acoroides .
Sentiment Analysis on Online Transportation Services Using Convolutional Neural Network Method

Online transportation services are public transportation that is much in demand by the public. According to the We Are Social 2020 report, as many as 21.7 million people in Indonesia use online transportation services. Customers or consumers often channel their opinions and complaints through various media. One of them is social media Instagram. On Instagram, online transportation services have an official account to provide the latest information about the service and collect comments from the public. When examined further, the collection of comments can be used as a sentiment analysis system. When assembled, we will conclude an online transportation service that has the best sentiment on Instagram. Therefore, the system created can analyze sentiments on online transportation service products using the CNN (Convolutional Neural Network) method. This system is expected to help consumers of online transportation services choose the best service from sentiment analysis. The results of this thesis in classifying sentiments in the Instagram comments column managed to get an accuracy of an average of 94%.
A novel light field compression framework with hybrid residue transform mechanism

To meet the challenge of compressing the vast light field data, many prediction-based algorithms achieve satisfactory results. Although these algorithms remove most redundancy through angular prediction, they rarely develop the suitable de-correlation solutions for the prediction residue, leading to a large gap for higher compression performance. Therefore, this letter proposes a hybrid residue transform (HRT) mechanism to fully exploit the correlations in prediction residue for a three-layer hierarchical light field compression. Specifically, the prediction residue is hierarchically generated from the authors' three-layer structure. Besides, considering the high-dimensional feature of light field residue, the high-dimensional transforms, including 3D and 4D discrete cosine transforms, are alternately utilized to make the energy of the residue more compact. Experimental results demonstrate that the proposed method outperforms the state-of-the-art methods.