Reviewerly/exHarmony huggingface dataset:
Real-time Pseudo-LiDAR 3D object detection with geometric constraints

Three-dimension (3D) object detection is an essential task in autonomous driving. Although recent LiDAR-based 3D object detection techniques have been well-studied and achieve high detection accuracy, the cost of LiDAR sensors causes a high premium for their practical implementation. Recently introduced Pseudo-LiDAR based methods that utilize image data to detect objects show great prospects for their high cost-effectiveness, however, they tend to be computational complexity and can not meet the realtime requirement. In this paper, we propose a light-weight Pseudo-LiDAR 3D detection system which achieves both high accuracy and high responsiveness. Specifically, we adopt an efficient depth estimator where Binary Neural Networks (BNN) is employed to achieve timely depth prediction. To tackle the accuracy degradation issue caused by the quantitation of the BNNs, we introduce the geometric constraints of virtual planes into the BNN training to enhance the completeness of the objects and improve their representation in 3D space. For the 3D object detector of our system, we provide effective improving schemes including a deviation-aware (DA) head and a finetuning module (FM) for converting existing LiDAR-based detectors to high efficient Pseudo-LiDAR detection components. Experiments on the KITTI benchmark show that our system can conduct the 3D detection within only 35 ms while achieving competitive results to the state-of-the-art (SOTA) algorithms.
Accurate Cooperative Sensor Fusion by Parameterized Covariance Generation for Sensing and Localization Pipelines in CAVs

A major challenge in cooperative sensing is to weight the measurements taken from the various sources to get an accurate result. Ideally, the weights should be inversely proportional to the error in the sensing information. However, previous cooperative sensor fusion approaches for autonomous vehicles use a fixed error model, in which the covariance of a sensor and its recognizer pipeline is just the mean of the measured covariance for all sensing scenarios. The approach proposed in this paper estimates error using key predictor terms that have high correlation with sensing and localization accuracy for accurate covariance estimation of each sensor observation. We adopt a tiered fusion model consisting of local and global sensor fusion steps. At the local fusion level, we add in a covariance generation stage using the error model for each sensor and the measured distance to generate the expected covariance matrix for each observation. At the global sensor fusion stage we add an additional stage to generate the localization covariance matrix from the key predictor term velocity and combines that with the covariance generated from the local fusion for accurate cooperative sensing. To showcase our method, we built a set of 1/10 scale model autonomous vehicles with scale accurate sensing capabilities and classified the error characteristics against a motion capture system. Results show an average and max improvement in RMSE when detecting vehicle positions of 1.42x and 1.78x respectively in a four-vehicle cooperative fusion scenario when using our error model versus a typical fixed error model.
Passive and Active Learning of Driver Behavior from Electric Vehicles

Modeling driver behavior provides several advantages in the automotive industry, including prediction of electric vehicle energy consumption. Studies have shown that aggressive driving can consume up to 30% more energy than moderate driving, in certain driving scenarios. Machine learning methods are widely used for driver behavior classification, which, however, may yield some challenges such as sequence modeling on long time windows and lack of labeled data due to expensive annotation. To address the first challenge, passive learning of driver behavior, we investigate non-recurrent architectures such as self-attention models and convolutional neural networks with joint recurrence plots (JRP), and compare them with recurrent models. We find that self-attention models yield good performance, while JRP does not exhibit any significant improvement. However, with the window lengths of 5 and 10 seconds used in our study, none of the non-recurrent models outperform the recurrent models. To address the second challenge, we investigate several active learning methods with different informativeness measures. We evaluate uncertainty sampling, as well as more advanced methods, such as query by committee and active deep dropout. Our experiments demonstrate that some active sampling techniques can outperform random sampling, and therefore decrease the effort needed for annotation.
DALi: Domain Adaptation in LiDAR Point Clouds for 3D Obstacle Detection

The use of modern LiDAR devices for onboard 3D object detection has paved the road for vehicles' full automation. Deep learning approaches leveraging the high-resolution spatial data from laser sweeps have become state-of-the-art in the field, providing an accurate representation of the traffic situation. Nonetheless, the need for a vast amount of annotated samples prevents a smooth deployment of these methods on custom sensor configurations, as differences in the scene geometry or the scanner specifications yield to major performance drops, especially in networks using the raw point cloud as input. To close this gap, an unsupervised domain adaptation training strategy is presented, so that features encoded by PointNet-like backbones are agnostic to the input domain. Through auxiliary domain classifiers and Gradient Reversal Layers, the accuracy of the model on the target domain is significantly boosted without the use of additional labels. Results on the KITTI and nuScenes benchmarks show a notable improvement over the baseline, demonstrating the suitability of GRLs for domain adaptation of 3D detectors based on raw LiDAR point clouds.
OmeTV Pretexting Phishing Attacks: A Case Study of Social Engineering

One of the most common types of social engineering attacks is phishing. This technique uses psychological manipulation of the target to unknowingly hand over the information the attacker wants. Our research tries to find out how a phishing attack can be executed by first pretexting the OmeTV video chat application. The target of our attack is OmeTV players from Indonesia who are over 18 years old. The proposed attack methodology is Social Engineering Session (SES). This study also aims to provide an overview of what kind of information can be extracted from the target during an attack. The results show that the pretexting phishing attack on the OmeTV video chat application was successfully carried out to obtain some of the target’s personal information including: full name, date of birth or age, address, educational status, hobbies, Instagram account, and phone number.
GCN-Based Topology Design for Decentralized Federated Learning in IoV

Decentralized federated learning (DFL) is a promising technology to implement distributed machine learning in Internet of Vehicles (IoV), which enables vehicles to share and aggregate models with their neighbors in a vehicle-to-vehicle (V2V) network. However, due to the high mobility of vehicles, model sharing via V2V links may fail as the topology of the V2V network is time-varying, which greatly reduces the efficiency of model aggregating and the speed of model training. To address this problem, in this paper, we propose a graph convolution network (GCN)-based topology design method, named G-DFL, to improve the training efficiency of DFL in IoV by properly selecting a subgraph of the underlay V2V network, which is referred to as overlay network, in each round of model sharing. First, by encoding the state of vehicles, we utilize a GCN to extract the features of V2V network topology to predict the effective V2V links for model sharing. In addition, to further reduce the delay of model training, we use Christofides' Algorithm to find the Hamiltonian circuit with the least delay as the overlay network. Simulation results validate that the proposed method significantly improves the model training performance in DFL compared with the other baseline methods.
Conversion of Entity-Relationship Model to NoSQL Document-Oriented Database Logical Model Using Workload Information and Entity Update Frequency

Although dubbed schema less, NoSQL databases, especially the document-oriented ones, can still make use of effective data modeling techniques in order to provide high-performance databases. In this paper, we present a proposal of converting Entity-Relationship Model (ER Model) into NoSQL document-oriented database logical model based on a workload-driven logical modeling algorithm by Lima & Mello [6]. This work improves the algorithm by considering, not only database workload, but also entity update frequency. The workload information and the entity update frequency are used to determine whether to refer or to embed in the logical model. Embedding or referencing are two prominent “relationships” among collections in document-oriented databases. We tested the improved conversion algorithm into a case study and the performance of the resulting logical model was tested on MongoDB in comparison to the logical model resulting from the original algorithm. The performance tests show that the logical model resulting from the improved algorithm provides better performance in read operations, but worse performance in create and update operations in collections with embedded relationships.
Hybrid Classical-Quantum Optimization for Ensemble Learning

Ensemble learning has gained attention due to its ability to improve predictive performance in classification and regression problems. However, there are several issues related to optimizing accuracy, diversity, and efficiency simultaneously in the ensemble learning process. Existing studies mostly focus on one or two of them, or focus on all three but only on the specific process of ensemble learning. Besides, the quantum approach has been expected to accelerate the optimization process while maintaining accuracy. The contribution of this study is to design a hybrid classical-quantum methodology for optimizing ensemble learning in a holistic way. This methodology combines classical (i.e. clustering, improved evaluation measures, and generalized weighted ensemble with internally tuned hyperparameters) and quantum (i.e. quantum annealing) approaches, and this methodology is analyzed to provide more optimal results.
Cyber-Physical Blockchain Based Secure Platforms for Data and Energy Trading in Multi-Level Electricity Markets

Despite all potential advantages of incorporating intelligence into power systems and electricity market, there is growing opposition towards the idea of smart grids following the threat of cyber-attacks that could cause serious issues in different parts of the power system if not properly addressed. In this regard, this paper focuses on introducing two novel effective conceptual secure platforms based on blockchain technology (BT) for multi-level electricity markets (MLEMs) that can be complied with the current electricity market conceptions. The proposed structures are evaluated in terms of consensus and time analyses and it is demonstrated that these strategies are scalable and compatible with the electricity market’s framework.
Identification of the FitzHugh-Nagumo Neuron Model Based on the Speed-Gradient and Filtering

The paper is devoted to identification of the FHN neuron model for the case when only measurements of the mem-brane potential are available. A new identification algorithm is proposed based on the speed-gradient methodology and filtering.
Visual Quality Assessment of Adversarially Attacked Images

With the growing interest in developing more effective and less perceivable adversarial attacks on images, many existing objective visual quality assessment metrics are being used in order to benchmark the performance of these attacks in terms of their imperceptibility. However, these objective metrics were originally developed for quantifying the image quality degradations due to other factors, such as acquisition, compression, and transmission, and there is no clear consensus on the effectiveness of these metrics in evaluating adversarial attacks, nor what the best metrics for such attacks are. This work proposes a subjective study where human subjects rate the visual quality of images that have been adversarially attacked using popular and contemporary adversarial attack methods, compared to their original form. The collected subjective scores along with the original and adversarially attacked images are used to construct a Subjective Testing Adversarial Attack Quality (STAAQ) database. The constructed database is used to assess the performance of popular objective metrics in terms of their ability to predict the visual quality of adversarially attacked images.
Factors affecting user experience of contact tracing app during COVID-19: an aspect-based sentiment analysis of user-generated review

This study aims to identify the critical factors influencing the user experience of contact tracing apps and the sentiments around them. For this purpose, we used Google play reviews of Aarogya Setu, a contact tracing app developed in India. First, we establish the relationship between review sentiment and review rating using regression between sentiment polarity and review rating. Then, we used a hybrid aspect-based sentiment analysis approach that uses unsupervised linguistic techniques to determine statistically significant concepts present in the review texts and cluster them into representative aspects that were then tagged under human supervision. Finally, supervised deep learning methods were applied for exhaustive extraction of the aspects and associated sentiments from the reviews. The final exercise of determining the key influencing factors was done by grouping these aspects under factors identified by marketing experts. A total of nine factors were identified, with the usefulness of the app being the most important factor. The findings of this study are essential for the development team and government to improve the application and increase adoption.
Insights from impacts of the digital divide on children in five majority world countries during the COVID-19 pandemic

The digital divide is especially pertinent in Majority World Countries (MWCs), and this was exacerbated greatly by the pandemic. Tackling the digital divide underpins the work of Human–Computer Interaction for Development (HCI4D) and remains an important global endeavour. Our project aimed to understand how children and young people (CYP) in MWC coped during the pandemic and how technology played a role. Voices of CYP were complemented by those of their parents and professionals with whom CYP interacted regularly. Our empirical study involved 73 CYP and 76 adults from Brazil, Kenya, Pakistan, South Africa, and Turkey. Qualitative data from diaries, drawings and focus groups were analysed thematically. Four major themes were identified – 'access', 'usage', 'risk', and 'future Among others, some intriguing findings were that CYP acutely felt the peer pressure on ICT ownership and tended to direct frustration at parents, who grappled with their untenable roles as gatekeepers to digital worlds. Implications for addressing the digital divide include long-term strategies to improve infrastructures and mobilise community-based collaborative efforts and enhance digital literacy.
IMGC-GNN: A multi-granularity coupled graph neural network recommendation method based on implicit relationships

In the application recommendation field, collaborative filtering (CF) method is often considered to be one of the most effective methods. As the basis of CF-based recommendation methods, representation learning needs to learn two types of factors: attribute factors revealed by independent individuals (e.g., user attributes, application types) and interaction factors contained in collaborative signals (e.g., interactions influenced by others). However, existing CF-based methods fail to learn these two factors separately; therefore, it is difficult to understand the deeper motivation behind user behaviors, resulting in suboptimal performance. From this point of view, we propose a multi-granularity coupled graph neural network recommendation method based on implicit relationships (IMGC-GNN). Specifically, we introduce contextual information (time and space) into user-application interactions and construct a three-layer coupled graph. Then, the graph neural network approach is used to learn the attribute and interaction factors separately. For attribute representation learning, we decompose the coupled graph into three homogeneous graphs with users, applications, and contexts as nodes. Next, we use multilayer aggregation operations to learn features between users, between contexts, and between applications. For interaction representation learning, we construct a homogeneous graph with user-context-application interactions as nodes. Next, we use node similarity and structural similarity to learn the deep interaction features. Finally, according to the learned representations, IMGC-GNN makes accurate application recommendations to users in different contexts. To verify the validity of the proposed method, we conduct experiments on real-world interaction data from three cities and compare our model with seven baseline methods. The experimental results show that our method has the best performance in the top-k recommendation.
LDP‐Fed+: A robust and privacy‐preserving federated learning based classification framework enabled by local differential privacy

Abstract As a distributed learning framework, Federated Learning (FL) allows different local learners/participants to collaboratively train a joint model without exposing their own local data, and offers a feasible solution to legally resolve data islands. However, among them, the data privacy and model security are two challenges. The former means that, if original data are used for trained FL models, various methods can be used to deduce the original data samples, thereby causing the leakage of data. The latter implies that unreliable/malicious participants may affect or destroy the joint FL model, through uploading wrong local model parameters. Therefore, this paper proposes a novel distributed FL training framework, namely LDP‐Fed+, which takes into account differential privacy protection and model security defense. Specifically, firstly, a local perturbation module is added at the local learner side, which perturbs the original data of local learners through feature extraction, binary encoding and decoding, and random response. Then, through using the perturbed data, local neural network model is trained to obtain the network parameters that meet local differential protection, to effectively deal with model inversion attacks. Secondly, a security defense module is added on the server side, which uses the auxiliary model and differential index mechanism to select an appropriate number of local disturbance parameters for aggregation to enhance model security defense and deal with membership inference attacks. The experimental results show that, compared with other federated learning models based on differential privacy, LDP‐Fed+ has stronger robustness for model security and higher accuracy for model training while ensuring strict privacy protection.
Designing Artificial Neural Network Using Particle Swarm Optimization: A Survey

Neural network modeling has become a special interest for many engineers and scientists to be utilized in different types of data as time series, regression, and classification and have been used to solve complicated practical problems in different areas, such as medicine, engineering, manufacturing, military, business. To utilize a prediction model that is based upon artificial neural network (ANN), some challenges should be addressed that optimal designing and training of ANN are major ones. ANN can be defined as an optimization task because it has many hyper parameters and weights that can be optimized. Metaheuristic algorithms such as swarm intelligence-based methods are a category of optimization methods that aim to find an optimal structure of ANN and to train the network by optimizing the weights. One of the commonly used swarm intelligence-based algorithms is particle swarm optimization (PSO) that can be used for optimizing ANN. In this study, we review the conducted research works on optimizing the ANNs using PSO. All studies are reviewed from two different perspectives: optimization of weights and optimization of structure and hyper parameters.
A multilayered Pauli tracking architecture for lattice surgery‐based logical qubits

Abstract In quantum computing, the use of Pauli frames through software traces of classical computers improves computation efficiency. In previous studies, error correction and Pauli operation tracking have been performed simultaneously using integrated Pauli frames in the physical layer. In such a complex processing structure, the number of simultaneous operations processed in the physical layer exponentially increases as the distance of the surface code encoding logical qubit increases. This study proposes a Pauli frame management architecture partitioned into two layers for a lattice surgery‐based surface code and describes its structure and operation rules. To evaluate the effectiveness of our method, we generated a random circuit according to the gate ratios constituting the commonly known quantum circuits and compared the generated circuit with the existing Pauli frame and our method. Simulations show a decrease of about 5% over traditional methods. In the case of experiments that only increase the code distance of the logical qubit, it can be seen that the effect of reducing the physical operation through the logical Pauli frame becomes more important.
Predictors of Intention to Use a Sustainable Cloud-Based Quality Management System among Academics in Jordan

This research aims to provide a predictive model of essential factors influencing the behavioral intention to use sustainable cloud-based quality management systems among academics in Jordan. A comprehensive research model was developed based on the Unified Theory of Acceptance and Use of Technology (UTAUT2) and the Theory of Planned Behavior (TPB), which was tested using cross-sectional data. The research sample covers Jordanian higher education institutions (23 governmental and private universities), and the unit of analysis includes 500 academics. The research adapts and modifies the UTAUT2 model and TPB to explain behavioral intention to use sustainable cloud-based quality management systems in developing countries. The proposed model explained 0.478 percent of behavioral intention variance and 0.127 percent of the user behavior variance. Three constructs are found to be significant predictors: perceived behavioral control, performance expectancy, and facilitating conditions. The attitude toward the behavior and subjective norm are not significant predictors. The research contributes to the literature in several ways. First, it extends previous studies by examining predictors of the behavioral intention to use SCQMS in higher education institutions. Second, it provides rigorous empirical evidence that incorporating the UTAUT2 model with the TPB produced a substantial improvement in the variance explained in behavioral intention compared to the prior research conducted in developing contexts. Third, this research provides useful insight into university management. The research provides a better understanding of the essential factors influencing the behavior intention to use sustainable cloud-based quality management systems in Jordanian Universities. Thus, the research model provides better explanatory power than previous studies in business literature and developing markets.