Reviewerly/exHarmony huggingface dataset:
ECO++: Adaptive deep feature fusion target tracking method in complex scene

Efficient Convolution Operator (ECO) algorithms have achieved impressive performances in visual tracking. However, its feature extraction network of ECO is unconducive for capturing the correlation features of occluded and blurred targets between long-range complex scene frames. More so, its fixed weight fusion strategy does not use the complementary properties of deep and shallow features. In this paper, we propose a new target tracking method, namely ECO++, using deep feature adaptive fusion in a complex scene, in the following two aspects: First, we constructed a new temporal convolution mode and used it to replace the underlying convolution layer in Conformer network to obtain an improved Conformer network. Second, we adaptively fuse the deep features, which output through the improved Conformer network, by combining the Peak to Sidelobe Ratio (PSR), frame smoothness scores and adaptive adjustment weight. Extensive experiments on the OTB-2013, OTB-2015, UAV123, and VOT2019 benchmarks demonstrate that the proposed approach outperforms the state-of-the-art algorithms in tracking accuracy and robustness in complex scenes with occluded, blurred, and fast-moving targets.
A novel algorithm for identifying the optimal CNN Architectures regulated by Swarm Intelligence

The Emergence of deep learning architectures in recent years paves a pathway for developing several real-time applications. Deep learning architecture selection is time-consuming since it takes hours to get trained, few architectures take days to get training. To address the pitfall in selecting the architectures, we developed a novel algorithm that returns an optimal architecture to the user with the help of swarm intelligence. The parameters required to run the algorithm have been obtained dynamically from the input samples, so the returned architecture is reliable. The algorithm is tested based on qualitative metrics such as training accuracy and testing accuracy, and the algorithm is evaluated based on quantitative metrics such as convergence time and execution time. The algorithm is robust against any dataset, and the architecture generated by the algorithm yields better results.
Performance Enhancement of Photo Voltaic System for Rural Electrification in Higher Altitude Region: A Case Study in Uttarakhand, India

One of the greatest challenges in today's world is to satisfy the energy needs of a growing population, in a sustainable way. In Developing countries, actual electrification of rural areas is hindered by high costs and unreliability of service, which often discourage rural communities to avail energy services even when they are available. This study aimed at better understanding the energy struggle of Indian rural communities, and propose efficient technological solutions to address it. Taking the case of a community located in the Himalaya region, a field based, participatory and bottom-up approach is taken to assess the community needs, expectations, and actual resources. A prototype of solar panel using machine learning-assisted automatic sun tracker system is proposed to provide sustainable and affordable electricity to the community.
Security Techniques Implementation on Big Data Using Steganography and Cryptography

The COVID-19 pandemic has increased everyone’s exposure to the Internet thus there has been an addition of new people who now access the Internet and its applications. Hence, the quantity of big data has grown tremendously in the era of smart city life. Thus concern for security of big data has risen. The attacks currently faced by big databases such as misuse, misrepresentation, modification, and unauthorized users and such attacks have increased in number. To ensure the security of big data stores to prevent misuse, misrepresentation, modification, and unauthorized users issues related to insecurity in storage units and not rendering it vulnerable to attackers, it is needed to enhance the secrecy, privacy, and increase the capacity for hiding secret cover. While cryptography guarantees authentication, integrity, non-repudiation, etc. In this paper, the author has studied popular data hiding techniques, especially steganography and cryptography used for provides security to big databases.
RSCOEWR: Radical-Based Sentiment Classification of Online Education Website Reviews

Abstract Online education is becoming more and more popular with the development of the Internet. In particular, due to the COVID-19 pandemic, many countries around the world are increasing the popularity of online education, which makes the research on sentiment classification of course reviews of online education websites an important research direction in natural language processing tasks. Traditional sentiment classification models are mostly based on English. Unlike English, Chinese characters are based on pictograms. Radicals of Chinese characters can also express certain semantics, and characters with the same radical often have similar meanings. Therefore, RSCOEWR, a word-level and radical-level based sentiment classification model for course reviews of Chinese online education websites is proposed, which solves the problem of data sparsity of reviews by feature extraction of multiple dimensions. In addition, a deep learning model based on CNN, BILSTM, BIGRU and Attention is constructed to solve the problem of high dimension and assigning the same attention to context of traditional sentiment classification model. Extensive comparative experiment results show that RSCOEWR outperforms the state-of-the-art sentiment classification models, and the experimental results on public Chinese sentiment classification datasets prove the generalization ability of RSCOEWR.
Robust P2P networking connectivity estimation engine for permissionless Bitcoin cryptocurrency

Blockchain relies on the underlying peer-to-peer (P2P) networking to broadcast and get up-to-date on the blocks and transactions. Because of the blockchain operations' reliance on the information provided by P2P networking, it is imperative to have high P2P connectivity for the quality of the blockchain system operations and performances. High P2P networking connectivity ensures that a peer node is connected to multiple other peers providing a diverse set of observers of the current state of the blockchain and transactions. However, in a permissionless Bitcoin cryptocurrency network, using the peer identifiers – including the current approach of counting the number of distinct IP addresses and port numbers – can be ineffective in measuring the number of peer connections and estimating the networking connectivity. Such current approach is further challenged by the networking threats manipulating identities. We build a robust estimation engine for the P2P networking connectivity by sensing and processing the P2P networking traffic. We take a systematic approach to study our engine and analyze the followings: the different components of the connectivity estimation engine and how they affect the accuracy performances, the role and the effectiveness of an outlier detection to enhance the connectivity estimation, and the engine's interplay with the Bitcoin protocol. We implement a working Bitcoin prototype connected to the Bitcoin mainnet to validate and improve our engine's performances and evaluate the estimation accuracy and cost efficiency of our connectivity estimation engine. Our results show that our scheme effectively counters the identity-manipulations threats, achieves 96.4% estimation accuracy with a tolerance of one peer connection, and is lightweight in the overheads in the mining rate, thus making it appropriate for the miner deployment.
A Steady-State Flight Control Algorithm Combining Stretching Ratio Coefficient and PID Control for UAVs in Uncertain Environments

Unmanned aerial vehicle (UAV) has the advantages of flexible operation, simple structure, and low price, which has been widely researched. In recent years, the application of small UAVs has become more extensive, and the steady-state control of UAVs has great research prospects and value due to it being the key to better execute flight task. A PID steady-state control algorithm based on color recognition and target detection is designed herein. Firstly, it is necessary to calculate the distance between the coordinates of the center of the UAV screen and the geometric center of the target point. Secondly, a pixel distance correction algorithm based on actual distance is proposed so as to correct pixel distance deviation. Finally, it is necessary to control the speed of the UAV by a PID control algorithm to achieve the goal that the UAV is stable near the geometric center of the target point. In short, this algorithm realizes the functions of real-time video transmission of the UAV, flight data storage, color recognition, and speed control of the UAV based on the PID control algorithm and distance correction. The experimental results demonstrate that the proposed algorithm has good robustness, makes the UAV have better stability, and can be used for the process of target tracking in uncertain environments.
Anomalous Behavior Detection Based on the Isolation Forest Model with Multiple Perspective Business Processes

Anomalous behavior detection in business processes inspects abnormal situations, such as errors and missing values in system execution records, to facilitate safe system operation. Since anomaly information hinders the insightful investigation of event logs, many approaches have contributed to anomaly detection in either the business process domain or the data mining domain. However, most of them ignore the impact brought by the interaction between activities and their related attributes. Based on this, a method is constructed to integrate the consistency degree of multi-perspective log features and use it in an isolation forest model for anomaly detection. First, a reference model is captured from the event logs using process discovery. After that, the similarity between behaviors is analyzed based on the neighborhood distance between the logs and the reference model, and the data flow similarity is measured based on the matching relationship of the process activity attributes. Then, the integration consistency measure is constructed. Based on this, the composite log feature vectors are produced by combining the activity sequences and attribute sequences in the event logs and are fed to the isolation forest model for training. Subsequently, anomaly scores are calculated and anomalous behavior is determined based on different threshold-setting strategies. Finally, the proposed algorithm is implemented using the Scikit-learn framework and evaluated in real logs regarding anomalous behavior recognition rate and model quality improvement. The experimental results show that the algorithm can detect abnormal behaviors in event logs and improve the model quality.
Trajectory Recovery Based on Interval Forward–Backward Propagation Algorithm Fusing Multi-Source Information

In the tracking scheme in which global navigation satellite system (GNSS) measurement is temporally lost or the sampling frequency is insufficient, dead reckoning based on the inertial measurement unit (IMU) and other location-related information can be fused as a supplement for real-time trajectory recovery. The tracking scheme based on interval analysis outputs interval results containing the ground truth, which gives it the advantage of convenience in multi-source information fusion. In this paper, a trajectory-recovery algorithm based on interval analysis is proposed, which can conveniently fuse GNSS measurement, IMU data, and map constraints and then output an interval result containing the actual trajectory. In essence, the location-related information such as satellite measurement, inertial data, and map constraints is collected by practical experiments and then converted into interval form. Thereby, the interval-overlapping calculation is performed through forward and backward propagation to accomplish the trajectory recovery. The practical experimental results show that the trajectory recovery accuracy based on the proposed algorithm performs better than the traditional Kalman filter algorithm, and the estimated interval results deterministically contain the actual trajectory. More importantly, the proposed interval algorithm is approved to be convenient to fuse additional location-related information.
New Classification Method for Independent Data Sources Using Pawlak Conflict Model and Decision Trees

The research concerns data collected in independent sets-more specifically, in local decision tables. A possible approach to managing these data is to build local classifiers based on each table individually. In the literature, many approaches toward combining the final prediction results of independent classifiers can be found, but insufficient efforts have been made on the study of tables' cooperation and coalitions' formation. The importance of such an approach was expected on two levels. First, the impact on the quality of classification-the ability to build combined classifiers for coalitions of tables should allow for the learning of more generalized concepts. In turn, this should have an impact on the quality of classification of new objects. Second, combining tables into coalitions will result in reduced computational complexity-a reduced number of classifiers will be built. The paper proposes a new method for creating coalitions of local tables and generating an aggregated classifier for each coalition. Coalitions are generated by determining certain characteristics of attribute values occurring in local tables and applying the Pawlak conflict analysis model. In the study, the classification and regression trees with Gini index are built based on the aggregated table for one coalition. The system bears a hierarchical structure, as in the next stage the decisions generated by the classifiers for coalitions are aggregated using majority voting. The classification quality of the proposed system was compared with an approach that does not use local data cooperation and coalition creation. The structure of the system is parallel and decision trees are built independently for local tables. In the paper, it was shown that the proposed approach provides a significant improvement in classification quality and execution time. The Wilcoxon test confirmed that differences in accuracy rate of the results obtained for the proposed method and results obtained without coalitions are significant, with a p level = 0.005. The average accuracy rate values obtained for the proposed approach and the approach without coalitions are, respectively: 0.847 and 0.812; so the difference is quite large. Moreover, the algorithm implementing the proposed approach performed up to 21-times faster than the algorithm implementing the approach without using coalitions.
High-capacity coverless image steganographic scheme based on image synthesis

In this paper, a coverless image steganographic scheme with high capacity is proposed. It can hide a color secret image into a color stego image with the same size. Furthermore, the stego image is synthesized without the aid of any cover images. Since there are no cover modification operations, it reduces the risk of being detected by steganalysis tools. The proposed scheme comprises a stego image synthesis sub-network (SynthesisNet) and a secret image recovery sub-network (RevealNet). In SynthesisNet, we apply the image-to-image translation to implement cross-domain image synthesis. It accepts source and reference images as the inputs, which can be used to customize the semantic and style of the stego image. A mapping between source and reference images is also designed to enhance the authenticity of the stego images. Experiments show that the proposed scheme provides high steganographic undetectability as well as good quality on stego and recovered secret images. Moreover, it can generate stego images with specified contents different from any existing images in the public channel.
A Novel Fuzzy Based Solar Irradiation Prediction Approach for Selection of Solar Panels in Hisar (Haryana)

This paper proposes an ANN-based framework for forecasting the daily solar irradiance with the meteorological data collected over 10 years for Hisar city, located in Haryana. Meteorological information like temperature, relative humidity, and average wind speed is used as input variables for estimating sun irradiation in order to obtain high accuracy under various weather situations. Meteorological information is used as input variables for estimating the solar irradiation in order to obtain acceptable accuracy in a variety of weather circumstances. In addition, it was suggested in this paper that the Artificial Neural Network (ANN) model be trained using the Harris Hawks Optimization (HHO) Algorithm. The following task is to choose the best solar panel (SP) out of the thousands that are currently on the market. Due to a crucial component like weather, choosing the finest SP is important. Hence, this paper introduces a score-based model for selecting the best panel by considering the different criteria of the eleven SPs that are used as test panels. The annual power produced by panel P11 is 79.67% better than panel P1. Finally, based on the average power generated, the proposed panel P11 is regarded as the best panel when compared to other panels.
Detection of Defects in Power Grid Inspection Images Based on Multi-scale Fusion

With the increasing application of visual application scenarios in various industries, power grid inspection system based on vision is widely used. However, the power grid intelligent inspection system has the problem of unsatisfactory accuracy of small target detection. To address this problem, this paper proposes a multi-scale fusion-based defect detection model for grid inspection images. The object features are first acquired by improving the CSPDarknet53 network using the attention mechanism. Second, the multi-scale feature fusion module is constructed to obtain more feature information. Finally, the defect detection model of the grid inspection image is constructed based on the multi-scale feature fusion module. The experimental results of our proposed algorithm are compared and analyzed with the current mainstream algorithms in all aspects. The optimal performance accuracy of our model reaches 0.927, which is improved by 6.06%.
APInf: Adaptive Policy Inference Based on Hierarchical Framework

Deep reinforcement learning has made adequate progress in addressing complex visual tasks. However, policy inference in relatively large neural networks is costly. Inspired by the fact that some states in a specific task can easily make decisions, we propose an APInf framework that can achieve low policy inference costs with quality guarantees. Two-stage training is proposed: First, to accelerate inference at easy states, sub-policy networks ordered by increasing capacity are generated; Second, to keep the quality of policy, master-policy is designed to choose a suitable sub-policy network subjected to state difficulty dynamically. We also improve the efficiency of master-policy inference by sharing the convolution layer between sub-policy networks and master-policy and then train master-policy under an extended MDP. Extensive experiments conducted in gym show that the adaptive inference framework reduces the FLOPs by 41.2% while maintaining similar quality. Furthermore, APInf outperforms the existing dynamic network structure in terms of policy quality and inference cost.
Predicting Molecular Subtype and Survival of Rhabdomyosarcoma Patients Using Deep Learning of H&amp;E Images: A Report from the Children's Oncology Group

Rhabdomyosarcoma (RMS) is an aggressive soft-tissue sarcoma, which primarily occurs in children and young adults. We previously reported specific genomic alterations in RMS, which strongly correlated with survival; however, predicting these mutations or high-risk disease at diagnosis remains a significant challenge. In this study, we utilized convolutional neural networks (CNN) to learn histologic features associated with driver mutations and outcome using hematoxylin and eosin (H&E) images of RMS.Digital whole slide H&E images were collected from clinically annotated diagnostic tumor samples from 321 patients with RMS enrolled in Children's Oncology Group (COG) trials (1998-2017). Patches were extracted and fed into deep learning CNNs to learn features associated with mutations and relative event-free survival risk. The performance of the trained models was evaluated against independent test sample data (n = 136) or holdout test data.The trained CNN could accurately classify alveolar RMS, a high-risk subtype associated with PAX3/7-FOXO1 fusion genes, with an ROC of 0.85 on an independent test dataset. CNN models trained on mutationally-annotated samples identified tumors with RAS pathway with a ROC of 0.67, and high-risk mutations in MYOD1 or TP53 with a ROC of 0.97 and 0.63, respectively. Remarkably, CNN models were superior in predicting event-free and overall survival compared with current molecular-clinical risk stratification.This study demonstrates that high-risk features, including those associated with certain mutations, can be readily identified at diagnosis using deep learning. CNNs are a powerful tool for diagnostic and prognostic prediction of rhabdomyosarcoma, which will be tested in prospective COG clinical trials.
Permaculture Landscape as an Adaptive Strategy towards Food Security at Community-Scale

Abstract Food security still becomes a big challenge nowadays, especially since the spreading of Covid-19 pandemic. Facts show that a large-scale social restriction policy in Indonesia limits the movement of logistics to distribute food. This situation tends to worsen due to the limited number of agricultural lands. Environmental or ecological sustainability in agriculture activity is also rarely considered, making the land poor for agriculture. Permaculture can be a viable approach to achieve sustainable food security, by creating a cultivated ecosystem which provides necessary human needs along with natural regeneration. Kuttab Al Fatih (KAF) Islamic School was chosen as the case study because contextually KAF has the potential to implement permaculture to its landscape development. Therefore, this paper discusses how to implement permaculture to KAF landscape development. This research was conducted with a literature review, field survey, and case study to determine permaculture design considerations within the selected project. The findings show that the permaculture landscape encourages the people to serve their needs (including foods) within the site without harming the environment. Through mimicking nature and adapting the pattern, permaculture system can create a sustainable ecosystem for people and other creatures to live in harmony. The implementation of permaculture at KAF should consider the Islamic values and the prohibitions in order the implementation does not conflict with user’s beliefs. User’s food needs, procurement, and human resources become another factor that should be considered to ensure the permaculture implementation is visible at KAF since the users are not staying at school.
Singular Value Decomposition (SVD) Co-training: A Semi-supervised Method for Object Detection

Existing object detection algorithms based on Convolutional Neural Network (CNN) generally rely on supervised learning with a large amount of labeled samples, which is very time-consuming and laborious. To address the problem, we propose a semi-supervised learning method by using Singular Value Decomposition (SVD) co-training. Specifically, this paper uses two mutually independent features which are obtained by SVD to guide the co-training of two detectors in one CNN model, meanwhile an adaptive self-labeling strategy is used to ensure the quality of pseudo labels. To verify the efficacy of SVD co-training, we conducted experiments on GMU, AVD and self-made BHID datasets. The result shows that SVD co-training could achieve the accuracy comparable to full-supervised learning by using only 5% of labeled data.