Reviewerly/exHarmony huggingface dataset:
Towards line-restricted dispatchable feeders using probabilistic forecasts for PV-dominated low-voltage distribution grids

The energy transition towards a fully renewables-based energy system calls for a massive deployment of photovoltaics (PV) in the low-voltage distribution grid (LVDG). I.e., PV panels have to be installed on every rooftop and perhaps even massive façade PV-installations are needed. For such a future PV-dominated scenario, the contribution of the present paper is twofold. First, since doubling line capacities in the LVDG is economically infeasible, we introduce the so-called line-restricted dispatchable feeder using probabilistic forecasts (LRDF) based on the existing notion of dispatchable feeder in the literature. By intelligent management of an appropriate battery, the proposed LRDF achieves dispatchability of distributed energy resources and inflexible loads while respecting the line restriction. Additionally, using probabilistic forecasts enables the proposed LRDF to consider uncertainties inherent in the electrical load and PV power generation. Second, we evaluate the LRDF using real-world data and probabilistic forecasts. The results show that the proposed line-restricted dispatchable feeder using probabilistic forecasts respects line restrictions and can improve line usage in PV-dominated low-voltage distribution grids.
Dual-Channel Localization Networks for Moment Retrieval with Natural Language

According to the given natural language query, moment retrieval aims to localize the most relevant moment in an untrimmed video. The existing solutions for this problem can be roughly divided into two categories based on whether candidate moments are generated: i) Moment-based approach: It pre-cuts the video into a set of candidate moments, performs multimodal fusion, and evaluates matching scores with the query. ii) Clip-based approach: It directly aligns video clips and query with predicting matching scores without generating candidate moments. Both frameworks have respective shortcomings: the moment-based models suffer from heavy computations, while the performance of clip-based models is familiarly inferior to moment-based counterparts. To this end, we design an intuitive and efficient Dual-Channel Localization Network (DCLN) to balance computational cost and retrieval performance. For reducing computational cost, we capture the temporal relations of only a few video moments with the same start or end boundary in the proposed dual-channel structure. The start or end channel map index represents the corresponding video moment's start or end time boundary. For improving model performance, we apply the proposed dual-channel localization network to efficiently encode the temporal relations on the dual-channel map and learn discriminative features to distinguish the matching degree between natural language query and video moments. The extensive experiments on two standard benchmarks demonstrate the effectiveness of our proposed method.
Machine Learning Based Content-Agnostic Viewport Prediction for 360-Degree Video

Accurate and fast estimations or predictions of the (near) future location of the users of head-mounted devices within the virtual omnidirectional environment open a plethora of opportunities in application domains such as interactive immersive gaming and tele-surgery. Therefore, the past years have seen growing attention to models for viewport prediction in 360֯ environments. Among the approaches, content-agnostic, trajectory-based methods have the potential to provide very fast solutions, as they do not require complex analysis of the videos to provide a prediction. However, accurate trajectory-based viewport prediction is rather difficult due to the intrinsic variability in user behaviour. Furthermore, even when making use of machine learning, current approaches tend to be brute-force and heavily tailored to specific datasets with little comparison to existing benchmarks or publicly available studies. This article presents a generic, content-agnostic viewport prediction method consisting of a window-based approach combined with a preprocessing system to classify behavioural patterns in terms of user clustering and trajectory correlation. Moreover, as the state of the art does not provide a comparative analysis of different approaches, this work contributes to this. Based on the obtained results, a combined prediction model is proposed and evaluated. Our method shows a 36.8% to 53.9% improvement when compared to the static prediction baseline for a prediction horizon of 8 seconds. In addition, a 11.5% to 24.0% improvement to a brute-force machine learning prediction approach is obtained. As such, this work contributes towards the creation of more generic and structured solutions for content-agnostic viewport prediction in terms of data representation, preprocessing and modelling.
Phishing Detection and Prevention using Chrome Extension

During pandemic COVID-19 outbreaks, number of cyber-attacks including phishing activities have increased tremendously. Nowadays many technical solutions on phishing detection were developed, however these approaches were either unsuccessful or unable to identify phishing pages and detect malicious codes efficiently. One of the downside is due to poor detection accuracy and low adaptability to new phishing connections. Another reason behind the unsuccessful anti-phishing solutions is an arbitrary selected URL-based classification features which may produce false results to the detection. Therefore, in this work, an intelligent phishing detection and prevention model is designed. The proposed model employs a self-destruct detection algorithm in which, machine learning, especially supervised learning algorithm was used. All employed rules in algorithm will focus on URL-based web characteristic, which attackers rely upon to redirect the victims to the simulated sites. A dataset from various sources such as Phish Tank and UCI Machine Learning repository were used and the testing was conducted in a controlled lab environment. As a result, a chrome extension phishing detection were developed based on the proposed model to help in preventing phishing attacks with an appropriate countermeasure and keep users aware of phishing while visiting illegitimate websites. It is believed that this smart phishing detection and prevention model able to prevent fraud and spam websites and lessen the cyber-crime and cyber-crisis that arise from year to year.
TLPGNN

Graph Neural Networks (GNNs) are an emerging class of deep learning models on graphs, with many successful applications, such as, recommendation systems, drug discovery, and social network analysis. The GNN computation includes both regular neural network operations and general graph convolution operations, which take the majority of the total computation time. Though several recent works have been proposed to accelerate the computation for GNNs, they face the limitations of heavy pre-processing, low efficient atomic operations, and unnecessary kernel launches. In this paper, we design TLPGNN, a lightweight two-level parallelism paradigm for GNN computation. First, we conduct a systematic analysis on the hardware resource usage of GNN workloads to deeply understand the specialties of GNN workloads. With the insightful observations, we then divide the GNN computation into two levels, i.e., vertex parallelism for the first level and feature par- allelism for the second. Next, we employ a novel hybrid dynamic workload assignment to address the imbalanced workload distribution. Furthermore, we fuse the kernels to reduce the number of kernel launches and cache the frequently accessed data into registers to avoid unnecessary memory traffics. Together, TLPGNN is able to significantly outperform existing GNN computation systems, such as DGL, GNNAdivsor, and FeatGraph, by 5.6×, 7.7×, and 3.3×, respectively, on the average.
Network reconstruction based on synthetic data generated by a Monte Carlo approach

Background: Network models are useful tools for researchers to simplify and understand investigated systems. Yet, the assessment of methods for network construction is often uncertain. Random resampling simulations can aid to assess methods, provided synthetic data exists for reliable network construction.&#x0D; Objectives: We implemented a new Monte Carlo algorithm to create simulated data for network reconstruction, tested the influence of adjusted parameters and used simulations to select a method for network model estimation based on real-world data. We hypothesized, that reconstructs based on Monte Carlo data are scored at least as good compared to a benchmark.&#x0D; Methods: Simulated data was generated in R using the Monte Carlo algorithm of the mcgraph package. Benchmark data was created by the huge package. Networks were reconstructed using six estimator functions and scored by four classification metrics. For compatibility tests of mean score differences, Welch’s t-test was used. Network model estimation based on real-world data was done by stepwise selection.&#x0D; Samples: Simulated data was generated based on 640 input graphs of various types and sizes. The real-world dataset consisted of 67 medieval skeletons of females and males from the region of Refshale (Lolland) and Nordby (Jutland) in Denmark.&#x0D; Results: Results after t-tests and determining confidence intervals (CI95%) show, that evaluation scores for network reconstructs based on the mcgraph package were at least as good compared to the benchmark huge. The results even indicate slightly better scores on average for the mcgraph package.&#x0D; Conclusion: The results confirmed our objective and suggested that Monte Carlo data can keep up with the benchmark in the applied test framework. The algorithm offers the feature to use (weighted) un- and directed graphs and might be useful for assessing methods for network construction.&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D;
Comparative study on the performance of different classification algorithms, combined with pre- and post-processing techniques to handle imbalanced data, in the diagnosis of adult patients with familial hypercholesterolemia

Familial Hypercholesterolemia (FH) is an inherited disorder of cholesterol metabolism. Current criteria for FH diagnosis, like Simon Broome (SB) criteria, lead to high false positive rates. The aim of this work was to explore alternative classification procedures for FH diagnosis, based on different biological and biochemical indicators. For this purpose, logistic regression (LR), naive Bayes classifier (NB), random forest (RF) and extreme gradient boosting (XGB) algorithms were combined with Synthetic Minority Oversampling Technique (SMOTE), or threshold adjustment by maximizing Youden index (YI), and compared. Data was tested through a 10 × 10 repeated k-fold cross validation design. The LR model presented an overall better performance, as assessed by the areas under the receiver operating characteristics (AUROC) and precision-recall (AUPRC) curves, and several operating characteristics (OC), regardless of the strategy to cope with class imbalance. When adopting either data processing technique, significantly higher accuracy (Acc), G-mean and F1 score values were found for all classification algorithms, compared to SB criteria (p < 0.01), revealing a more balanced predictive ability for both classes, and higher effectiveness in classifying FH patients. Adjustment of the cut-off values through pre or post-processing methods revealed a considerable gain in sensitivity (Sens) values (p < 0.01). Although the performance of pre and post-processing strategies was similar, SMOTE does not cause model's parameters to loose interpretability. These results suggest a LR model combined with SMOTE can be an optimal approach to be used as a widespread screening tool.
Designing a Secure Blockchain-Based Supply Chain Management Framework

Supply chain management (SCM) faces a critical security issue because of the asymmetry of information delivered to various parties in the ecosystem and the lack of corresponding supervision. In response, we propose the use of blockchain technology to address the SCM security issues and put forward a blockchain-based SCM framework. We apply design science paradigm to guide the blockchain-based SCM framework development and implementation of a proof-of-concept prototype. We use Hyperledger Fabric and Composer to develop the prototype artifact. Performance evaluation results issued from Hyperledger Caliper prove the superiority and robustness of the proposed blockchain-based framework in terms of security and efficiency requirements, and performance metrics including throughput and latency. Also, the evaluation results show that the IT artifact is stable, and the high stability can reduce the risks of system vulnerabilities and breakdown.
The flowing nature matters: feature learning from the control flow graph of source code for bug localization

Bug localization plays an important role in software maintenance. Traditional works treat the source code from the lexical perspective, while some recent researches indicate that exploiting the program structure is beneficial for improving bug localization. Control flow graph (CFG) is a widely used graph representation, which essentially represents the program structure. Although using graph neural network for feature learning is a straightforward way and has been proven effective in various software mining problems, this approach is inappropriate since adjacent nodes in the CFG could be totally unrelated in semantics. On the other hand, previous statements may affect the semantics of subsequent statements along the execution path, which we call the flowing nature of control flow graph. In this paper, we claim that the flowing nature should be explicitly considered and propose a novel model named cFlow for bug localization, which employs a particular designed flow-based GRU for feature learning from the CFG. The flow-based GRU exploits the program structure represented by the CFG to transmit the semantics of statements along the execution path, which reflects the flowing nature. Experimental results on widely-used real-world software projects show that cFlow significantly outperforms the state-of-the-art bug localization methods, indicating that exploiting the program structure from the CFG with respect to the flowing nature is beneficial for improving bug localization.
A Multiobjective Hybrid Optimization Algorithm for Path Planning of Coal Mine Patrol Robot

In the complex underground environment, the paths planned for coal mine patrol robot are often too long and unsmooth under the influence of low visibility and poor road conditions. To solve the problems, this paper improves the hybrid algorithm between the improved artificial fish swarm algorithm (AFSA) and the dynamic window algorithm (DWA) for global path planning of coal mine patrol robot and introduces the improved genetic algorithm (GA) to enhance the path planning accuracy. Based on the global optimal path, the improved DWA was adopted to design a new adaptive trajectory evaluation function, which improves the ability of the patrol robot to avoid local obstacles. The proposed optimization algorithm was proved feasible through simulations. In addition, a simulation platform for the control of coal mine patrol robot was established, using the software development platform for coal mine patrol robot and robot operating system (ROS). The simulation results show that the improvement shortened the path length by 0.12 m, reduced the time by 3.14 s, and removed many turning points and redundant points. Therefore, the proposed improved hybrid path planning algorithm is effective and superior.
MSSPQ: Multiple Semantic Structure-Preserving Quantization for Cross-Modal Retrieval

Cross-modal hashing is a hot issue in the multimedia community, which is to generate compact hash code from multimedia content for efficient cross-modal search. Two challenges, i.e., (1) How to efficiently enhance cross-modal semantic mining is essential for cross-modal hash code learning, and (2) How to combine multiple semantic correlations learning to improve the semantic similarity preserving, cannot be ignored. To this end, this paper proposed a novel end-to-end cross-modal hashing approach, named Multiple Semantic Structure-Preserving Quantization (MSSPQ) that is to integrate deep hashing model with multiple semantic correlation learning to boost hash learning performance. The multiple semantic correlation learning consists of inter-modal and intra-modal pairwise correlation learning and Cosine correlation learning, which can comprehensively capture cross-modal consistent semantics and realize semantic similarity preserving. Extensive experiments are conducted on three multimedia datasets, which confirms that the proposed method outperforms the baselines.
Model-Driven Deep Learning-Based MIMO-OFDM Detector: Design, Simulation, and Experimental Results

Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM), a fundamental transmission scheme, promises high throughput and robustness against multipath fading. However, these benefits rely on the efficient detection strategy at the receiver and come at the expense of the extra bandwidth consumed by the cyclic prefix (CP). We use the iterative orthogonal approximate message passing (OAMP) algorithm in this paper as the prototype of the detector because of its remarkable potential for interference suppression. However, OAMP is computationally expensive for the matrix inversion per iteration. We replace the matrix inversion with the conjugate gradient (CG) method to reduce the complexity of OAMP. We further unfold the CG-based OAMP algorithm into a network and tune the critical parameters through deep learning (DL) to enhance detection performance. Simulation results and complexity analysis show that the proposed scheme has significant gain over other iterative detection methods and exhibits comparable performance to the state-of-the-art DL-based detector at a reduced computational cost. Furthermore, we design a highly efficient CP-free MIMO-OFDM receiver architecture to remove the CP overhead. This architecture first eliminates the intersymbol interference by buffering the previously recovered data and then detects the signal using the proposed detector. Numerical experiments demonstrate that the designed receiver offers a higher spectral efficiency than traditional receivers. Finally, over-the-air tests verify the effectiveness and robustness of the proposed scheme in realistic environments.
HAR-GCNN: Deep Graph CNNs for Human Activity Recognition From Highly Unlabeled Mobile Sensor Data

The problem of human activity recognition from mobile sensor data applies to multiple domains, such as health monitoring, personal fitness, daily life logging, and senior care. A critical challenge for training human activity recognition models is data quality. Acquiring balanced datasets containing accurate activity labels requires humans to correctly annotate and potentially interfere with the subjects&#x2019; normal activities in real-time. Despite the likelihood of incorrect annotation or lack thereof, there is often an inherent chronology to human behavior. For example, we take a shower after we exercise. This implicit chronology can be used to learn unknown labels and classify future activities. In this work, we propose HAR-GCCN, a deep graph CNN model that leverages the correlation between chronologically adjacent sensor measurements to predict the correct labels for unclassified activities that have at least one activity label. We propose a new training strategy enforcing that the model predicts the missing activity labels by leveraging the known ones. HAR-GCCN shows superior performance relative to previously used baseline methods, improving classification accuracy by about 25% and up to 68% on different datasets.
A novel detail weighted histogram equalization method for brightness preserving image enhancement based on partial statistic and global mapping model

Histogram equalization (HE) is a classic and widely used image contrast enhancement algorithm for its good performance and high efficiency. However, over-enhancement caused by high peak in the histogram affects the subjective quality of the image processed by HE to a large extent. In this paper, a detail weighted histogram equalization (DWHE) method is proposed based on a novel histogram modification (HM) model named partial statistic and global mapping (PSGM) to alleviate high peak and suppress over-enhancement. Moreover, the authors implement a refined version of gamma correction (GC) named texture enhancement function (TEF) on high-frequency images to reduce the noise amplification effect. At last, the authors propose a novel adaptively weighted pixel-level image fusion method to further reduce the phenomenon of over-enhancement and improve brightness distribution. Both subjective and quantitative evaluations are conducted on images containing a variety of scenes. Compared with several state-of-the-art image enhancement methods, the proposed framework obtained generally the best performance in aspects of both subjective appearance and objective evaluation indices. Therefore, it is proved that the proposed methods can effectively alleviate the over-enhancement, enrich image details, and efficiently improve the visual quality while preserving the brightness of the image.
A Very Fast Copy-Move Forgery Detection Method for 4K Ultra HD Images

Copy-move forgery detection is a challenging task in digital image forensics. Keypoint-based detection methods have proven to be very efficient to detect copied-moved forged areas in images. Although these methods are effective, the keypoint matching phase has a high complexity, which takes a long time to detect forgeries, especially for very large images such as 4K Ultra HD images. In this paper, we propose a new keypoint-based method with a new fast feature matching algorithm, based on the generalized two nearest-neighbor (g2NN) algorithm allowing us to greatly reduce the complexity and thus the computation time. First, we extract keypoints from the input image. After ordering them, we perform a match search restricted to a window around the current keypoint. To detect the keypoints, we propose not to use a threshold, which allows low intensity keypoint matching and a very efficient detection of copy-move forgery, even in very uniform or weakly textured areas. Then, we apply a new matching algorithm, and finally we compute the cluster thanks to the DBSCAN algorithm. Our experimental results show that the method we propose can detect copied-moved areas in forged images very accurately and with a very short computation time which allows for the fast detection of forgeries on 4K images.
A2AE: Towards adaptive multi-view graph representation learning via all-to-all graph autoencoder architecture

The multi-view graph is a fundamental data model, which is used to describe complex networks in the real world. Learning the representation of multi-view graphs is a vital step for understanding complex systems and extracting knowledge accurately. However, most existing methods focus on a certain view or simply add multiple views together, which prevents them from making the best of the rich relational information in multiple views and ignores the importance of different views. In this paper, a novel all-to-all graph autoencoder is proposed for multi-view graph representation learning, namely A2AE. The all-to-all model first embeds the attribute multi-view graph into compact representations by semantic fusing the view-specific compact representations from multi-encoders, and then multi-decoders are trained to reconstruct graph structure and attributes. Finally, a self-training clustering module is attached for clustering tasks.