Reviewerly/exHarmony huggingface dataset:
BTG: A Bridge to Graph machine learning in telecommunications fraud detection

Telecommunications fraud runs rampant recently around the world. Therefore, how to effectively detect fraudsters has become an increasingly challenging problem. However, previous studies either assume that the samples are independent of each other and use non-graph methods, or use local subgraphs with good connectivity for graph-based anomaly detection. Few prior works have performed graph-based fraud detection on real-world Call Detail Records (CDR) meta data sets with sparse connectivity. To solve this problem, we propose an end-to-end telecommunications fraud detection framework named Bridge To Graph (BTG). BTG leverages the subscriber synergy behavior to reconstruct connectivity, which bridges the gap between sparse connectivity data and graph machine learning. Concretely, we extract multi-model features from meta data and perform Box–Cox transformation first. Then, aiming at the sparse connectivity of real-world CDR meta data, the graph is reconstructed through dimensionally selectable link prediction of node similarity. Finally, the reconstructed graph and node features are input into the graph machine learning module for node embedding representation learning and fraud node classification. Comprehensive experiments on the real-world telecommunications network CDR data set show that our proposed method outperforms the classic methods in many metrics. Beyond telecom fraud detection, our method can also be extended to anomaly detection scenarios with no graph or sparse connectivity graph.
An image encryption scheme using a chaotic neural network and a network with multistable hyperchaos

In this paper a new image encryption scheme is proposed, by which the pixel values are bit-level shuffled by the encryption sequences generated by a complex network, while the pixel positions of diffused images are scrambled by the chaotic sequences generated by a time-delayed neural network. In this way the key space is greatly enlarged through the simultaneous use of the time-delayed neural network and the complex network with a high dimensional multistable hyperchaotic system as nodal model. In particular, different nodes in the complex network are selected to generate the encryption and the decryption sequences. To achieve the synchronization accuracy necessarily for correct decryption, the number of nodes needed in the fully connected complex network is investigated and determined. Through applications on different kinds of plain images, the proposed algorithm exhibits excellent performance on security, i.e., with better statistical and differential characteristics, better plain-image key sensitivity and a large enough key space. In addition the encryption scheme can also resist various knowing attacks.
Detecting shilling groups in online recommender systems based on graph convolutional network

Online recommender systems have been shown to be vulnerable to group shilling attacks in which attackers of a shilling group collaboratively inject fake profiles with the aim of increasing or decreasing the frequency that particular items are recommended. Existing detection methods mainly use the frequent itemset (dense subgraph) mining or clustering method to generate candidate groups and then utilize the hand-crafted features to identify shilling groups. However, such two-stage detection methods have two limitations. On the one hand, due to the sensitivity of support threshold or clustering parameters setting, it is difficult to guarantee the quality of candidate groups generated. On the other hand, they all rely on manual feature engineering to extract detection features, which is costly and time-consuming. To address these two limitations, we present a shilling group detection method based on graph convolutional network. First, we model the given dataset as a graph by treating users as nodes and co-rating relations between users as edges. By assigning edge weights and filtering normal user relations, we obtain the suspicious user relation graph. Second, we use principal component analysis to refine the rating features of users and obtain the user feature matrix. Third, we design a three-layer graph convolutional network model with a neighbor filtering mechanism and perform user classification by combining both structure and rating features of users. Finally, we detect shilling groups through identifying target items rated by the attackers according to the user classification results. Extensive experiments show that the classification accuracy and detection performance (F1-measure) of the proposed method can reach 98.92% and 99.92% on the Netflix dataset and 93.18% and 92.41% on the Amazon dataset.
Image Compression Techniques in Wireless Sensor Networks: A Survey and Comparison

There is continuous intensive research on image compression techniques in wireless sensor networks (WSNs) in the literature.Some of the image compression techniques in WSNs that exist in the literature include discrete cosine transform (DCT), discrete waveform transforms (DWT), set partitioning in a hierarchical tree (SPIHT), and embedded zero tree wavelet (EZW) coding.Research on image compression in WSNs is necessitated by the need to improve the energy efficiency of sensor nodes and WSNs' lifetimes without compromising the quality of the reconstructed data.Several approaches have been developed centered around image compression and other factors in trying to limit the energy consumption of sensor nodes.Most of these approaches do not provide the error-bound mechanism that balances the rate of compression and distortion of the reconstructed image.Therefore, in this paper, a review and analysis of image compression techniques and approaches in WSNs are conducted.Available image compression approaches in WSNs in literature were then classified according to the image compression technique adopted, and their strengths and weaknesses were highlighted.In addition, a rate-distortion balanced data compression algorithm with error bound mechanism based on artificial neural networks (ANN) in the form of an autoencoder (AE) was coded and simulated in MATLAB before being evaluated and compared to the conventional approaches.The experimental results show that the simulated algorithm has less root mean square error (RMSE) and a higher coefficient of determination ( 2 ) values on variable compression ratios as compared to the Principal Component Analysis (PCA), Discrete Cosine Transform, and Fast Fourier Transform (FFT) when using the Grand-St-Bernard metrological dataset.Furthermore, it presented less RMSE, and higher compression ratio values compared to the Lightweight Temporal Compression (LTC) algorithm on variable error bounds when using the LUCE metrological dataset.Therefore, it was found that the simulated algorithm presents better compression fidelity as compared to the conventional approaches without an error-bound mechanism.Moreover, the algorithm analyzed presents a significant approach to balancing the compression ratio and reconstructed data quality through its error-bound mechanism.
LaF: Lattice-Based and Communication-Efficient Federated Learning

Federated learning is an emerging technology which allows a server to train a global model with the cooperation of participants without exposing the participants&#x2019; data. In recent years, there have been many studies focusing on maintaining participant privacy against honest-but-curious servers. In 2017, Google proposed a promising solution that applies double masking and secret sharing tools to protect participants&#x2019; gradients for each round of federated learning (CCS&#x2019;17). However, this solution fails to achieve post-quantum security and costs high communication overhead to distribute secret shares. To address this problem, this work designs a lattice-based multi-use secret sharing scheme to avoid distributing new secret shares to all participants in each round of federated learning while achieving post-quantum security. In other words, this new tool allows each participant to update his secret shares locally while maintaining the privacy of participants&#x2019; gradients against quantum attacks. Finally, this work applies this new secret sharing technique to construct a lattice-based federated learning protocl <i>LaF</i>. The theoretic analysis demonstrates that <i>LaF</i> saves a lot of communication costs compared with Google&#x2019;s solution, and the experimental results show that <i>LaF</i> achieves higher runtime efficiency.
Dynamic Gesture Recognition of A-Mode Ultrasonic Based on the DTW Algorithm

At present, the research on gesture recognition based on ultrasonic signals is mostly about static gestures. Compared with static gestures, dynamic gestures reflect human intent more intuitively. However, the speed difference of dynamic gestures will cause a mismatch in time series. To tackle this problem, we proposed a dynamic gesture recognition scheme based on the dynamic time warping (DTW) algorithm. First, the ultrasonic signals of the forearm muscles were acquired by four A-mode ultrasonic transducers. Second, the original signals underwent Gaussian filtering, feature extraction, normalization, and principal component analysis (PCA) dimensionality reduction to obtain the main feature information and convert it into time series. Finally, the time series of dynamic gestures were matched based on the DTW algorithm, and the minimum Euclidean distance between different time series was calculated for prediction. We tested five dynamic gestures on ten subjects. The experiments were conducted for a total of ten rounds, and only one round was used as the training set. Results reveal that using the max feature can get an accuracy of 88.0% ± 5.9%. In addition, the energy feature (87.5% ± 6.4%) and the mean feature (85.2% ± 6.9%) also performed well. This research realizes the dynamic gesture recognition of A-mode ultrasonic for the first time, proving the potential of ultrasonic signals, and providing a wider range of use scenarios.
SwiftAgg: Communication-Efficient and Dropout-Resistant Secure Aggregation for Federated Learning with Worst-Case Security Guarantees

We propose SwiftAgg, a novel secure aggregation protocol for federated learning systems, where a central server aggregates local models of N distributed users, each of size L, trained on their local data, in a privacy-preserving manner. Compared with state-of-the-art secure aggregation protocols, SwiftAgg significantly reduces the communication overheads without any compromise on security. Specifically, in presence of at most D dropout users, SwiftAgg achieves a server communication load of (T +1)L and a per-user communication load of up to (T+D+1)L, with a worst-case information-theoretic security guarantee, against any subset of up to T semi-honest users who may also collude with the curious server. The key idea of SwiftAgg is to partition the users into groups of size T+D+1, then in the first phase, secret sharing and aggregation of the individual models are performed within each group, and then in the second phase, model aggregation is performed on T +D+1 sequences of users across the groups. If a user in a sequence drops out in the second phase, the rest of the sequence remains silent. This design allows only a subset of users to communicate with each other, and only the users in a single group to directly communicate with the server, eliminating the requirements of 1) all-to-all communication network across users; and 2) all users communicating with the server, for other secure aggregation protocols. This helps to substantially slash the communication costs of the system.
Combining Long-Term Recurrent Convolutional and Graph Convolutional Networks to Detect Phishing Sites Using URL and HTML

Phishing, a well-known cyber-attack practice has gained significant research attention in the cyber-security domain for the last two decades due to its dynamic attacking strategies. Although different solutions have been exercised against phishing, phishing attacks have dramatically increased in the past few years. Recent studies have shown that machine learning has become prominent in the present anti-phishing context, and the techniques like deep learning have extensively improved anti-phishing tools&#x2019; detection ability. This paper proposes PhishDet, a new way of detecting phishing websites through Long-term Recurrent Convolutional Network and Graph Convolutional Network using URL and HTML features. PhishDet is the first of its kind, which uses the powerful analysis and processing capabilities of Graph Neural Network in the anti-phishing domain and recorded 96.42% detection accuracy, with a 0.036 false-negative rate. It is effective against zero-day attacks, and the average detection time which is 1.8 seconds could also be considered realistic. The feature selection of PhishDet is automatic and occurs inside the system, as PhishDet gradually learns URLs and HTML content features to handle constantly changing phishing attacks. This has outperformed similar solutions by achieving a 99.53% f1-score with a public benchmark dataset. However, PhishDet requires periodic retraining to maintain its performance over time. If such retraining could be facilitated, PhishDet could fight against phishers for a more extended period to safeguard Internet users from this Internet threat.
Keyword search over encrypted cloud data based on blockchain in smart medical applications

In consideration of the inconvenience of sharing user identity authorization, and the lack of correctness verification for searching electronic medical records in hospitals under the many-to-many search model, we propose an blockchain-aided searchable attribute-based encryption scheme that supports verification, we apply the scheme to electronic health record systems. Using blockchain technology, the ciphertext document is stored in the medical cloud, the secure index is stored in the blockchain, and the smart contract is used for searching, which reduces the computational burden of the medical cloud. In addition, this scheme can verify whether the results returned by the cloud server are correct, ensure data integrity and confidentiality. The scheme eliminates duplicate data and reduces the storage space of the medical cloud. Furthermore, users’ privacy security is guaranteed due to the hiding of access policy. The security analysis shows that the proposed scheme satisfies the semantic security of adaptively chosen-keyword attacks, can effectively protect the confidentiality of medical data and anonymity of users. Performance analysis and experiments demonstrate that the proposed scheme achieves optimizations in security index generation, trapdoor generation, search efficiency and result verification; hence, it is more suitable for many-to-many search scenarios such as smart medical applications. It effectively realizes the sharing of patient electronic medical records between doctors and third-party users without infringing on patient privacy.
Fast and accurate multi-class geospatial object detection with large-size remote sensing imagery using CNN and Truncated NMS

Multi-class geospatial object detection with remote sensing imagery has broad prospects in urban planning, natural disaster warning, industrial production, military surveillance and other applications. Accuracy and efficiency are two common measures for evaluating object detection models, and it is often difficult to achieve both at the same time. Developing a practical remote sensing object detection algorithm that balances the accuracy and efficiency is thus a big challenge in the Earth observation community. Here, we propose a comprehensive high-speed multi-class remote sensing object detection method. Firstly, we obtain a multi-volume YOLO (You Only Look Once) v4 model for balancing speed and accuracy, based on a pruning strategy of the convolutional neural network (CNN) and the one-stage object detection network YOLO v4. Moreover, we apply the Manhattan-Distance Intersection of Union (MIOU) loss function to the multi-volume YOLO v4 to further improve the accuracy without additional computational burden. Secondly, mainly due to computing limitations, a remote sensing image that is large-size relative to a natural image must first be divided into multiple smaller tiles, which are then detected separately, and finally, the detection results are spliced back to match the original image. In the process of remote sensing image slicing, a large number of truncated objects appear at the edge of tiles, which will produce a large number of false results in the subsequent detection links. To solve this problem, we propose a Truncated Non-Maximum Suppression (NMS) algorithm to filter out repeated and false detection boxes from truncated targets in the spliced detection results. We compare the proposed algorithm with the state-of-the-art methods on the Dataset for Object deTection in Aerial images (DOTA) and DOTA v2. Quantitative evaluations show that mAP and FPS reach 77.3 and 35 on DOTA, and 61.0 and 74 on DOTA v2. Overall, our method reaches the optimal balance between efficiency and accuracy, and realizes the high-speed remote sensing object detection.
Leveraging Class Balancing Techniques to Alleviate Algorithmic Bias for Predictive Tasks in Education

Predictive modeling is a core technique used in tackling various tasks in learning analytics research, e.g., classifying educational forum posts, predicting learning performance, and identifying at-risk students. When applying a predictive model, it is often treated as the first priority to improve its prediction accuracy as much as possible. Class balancing, which aims to adjust the unbalanced data samples of different class labels before using them as input to train a predictive model, has been widely regarded as a powerful method for boosting prediction accuracy. However, its impact on algorithmic bias remains largely unexplored, i.e., whether the use of class balancing methods would alleviate or amplify the differentiated prediction accuracy received by different groups of students (e.g., female versus male). To fill this gap, our study selected three representative predictive tasks as the testbed, based on which we 1) applied two well known metrics (i.e., hardness bias and distribution bias) to measure data characteristics to which algorithmic bias might be attributed; and 2) investigated the impact of a total of eleven class balancing techniques on prediction fairness. Through extensive analysis and evaluation, we found that class balancing techniques, in general, tended to improve predictive fairness between different groups of students. Furthermore, class balancing techniques (e.g., SMOTE and ADASYN), which add samples to the minority group (i.e., oversampling) can enhance the predictive accuracy of the minority group while not negatively affecting the majority group. Consequently, both fairness and accuracy can be improved by applying these oversampling class balancing methods. All data and code used in this study are publicly accessible via <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/lsha49/FairCBT</uri> .
An Improving Faster-RCNN With Multi-Attention ResNet for Small Target Detection in Intelligent Autonomous Transport With 6G

Numerous object detection algorithms, such as Faster RCNN, YOLO and SSD, have been extensively applied to various fields. Both accuracy and speed of the algorithms have been significantly improved. However, as 6G technology develops, the detection effect for the small object detection task in intelligent autonomous transportation is not ideal. To strengthen the detection ability and performance of multiple scales, especially for small objects, this study proposed an object detection model based on multi-attention residual network (MA-ResNet). At first, residual network with spatial attention, channel attention, and self-attention were designed as MA-ResNet. Meanwhile, the dataset labels were smoothed. On this basis, the proposed MA-ResNet replaced the original feature extractor VGG-16 of Faster-RCNN. Different layers of MA-ResNet were extracted for feature pyramid construction. Moreover, the improved Faster-RCNN model for object detection based on MA-ResNet was formed. Furthermore, the effectiveness of the model was confirmed. The results demonstrate that MA-ResNet outperforms other feature extraction models with faster convergence speed, higher accuracy, and stronger small object classification accuracy. The improved Faster-RCNN model for object detection can effectively improve the network retrieval accuracy, performance and robustness, can exhibit satisfying adaptability for the targets of different scales in different scenarios such as vehicle identification, autonomous driving in intelligent autonomous transport system with 6G. Moreover, the study provides a certain reference for improving the effect of multiple scales, especially small object detection in intelligent transportation with 6G.
A comparative techno-economic assessment of manually adjustable tilt mechanisms and automatic solar trackers for behind-the-meter PV applications

Many studies in the literature aim to increase the amount of solar radiation falling on photovoltaic (PV) panel surface to improve its performance. Most of these studies concentrate on solar tracking systems and few studies focus on manually adjustable tilt mechanisms. However, no studies in the literature compare these two methods techno-economically. Therefore, as its main contribution, this study makes a techno-economic comparison of solar trackers and manually adjustable tilt mechanisms. First, the electricity production of fixed-tilt, manually adjustable tilt mechanisms (monthly and seasonal adjustment), and automatic solar trackers (single-axis east-west (SA-EWT), single-axis south-north (SA-SNT), and dual-axis (DAT)) systems are technically analyzed for three provinces in Turkey with different solar characteristics. After that, the systems are compared economically and evaluated over levelized cost of electricity (LCoE), discounted payback period (DPBP), and internal rate of return (IRR). Finally, a detailed sensitivity analysis is made and the impact of changes in initial investment costs and real interest rates is examined. Eventually, the payback period of fixed-tilt systems is found to be 10.3–13.3 years in Turkey. Dual-axis solar trackers provide the highest electricity production increase (30.4–34.6%) compared to fixed-tilt but with the highest payback period (16.7–24 years) among all alternatives. Monthly manual tilt adjustment provides the most feasible solution by decreasing the payback period of fixed-tilt systems by around 8 months to 9.6–12.6 years and with an electricity production increase of 3.6–5%. • Techno-economic comparison of manual tilt adjustment and automatic solar tracking. • The most feasible alternative is monthly manual tilt adjustment. • Payback period of fixed-tilt system is 10.3–13.3 years in Turkey. • Monthly tilting increases production by 3.6–5%, payback period is 9.6–12.6 years. • 2-axis tracker increases production by 30.4–34.6%, payback period is 16.7–24 years.