Reviewerly/exHarmony huggingface dataset:
A multi-objectives framework for secure blockchain in fog-cloud network of vehicle-to-infrastructure applications

The Intelligent Transport System (ITS) is an emerging paradigm that offers numerous services at the infrastructure level for vehicle applications. Vehicle-to-infrastructure (V2I) is an advanced form of ITS where diverse vehicle services are deployed on the roadside unit. V2I consists of distributed computing nodes where transport applications are parallel processed. Many research challenges exist in the presented V2I paradigms regarding security, cyber-attacks, and application processing among heterogeneous nodes. These cyber-attacks, Sybil attacks, and their attempts cause a lack of security and degrade the V2I performance in the presented paradigms. This paper presents a new secure blockchain framework that handles cyber-attacks, as mentioned earlier. This paper formulates this complex problem as a combinatorial problem, encompassing concave and convex problems. The convex function minimizes the given constraints, such as time and security risk, and the concave function improves performance and accuracy. Therefore, numerous constraints, such as time, energy, malware detection accuracy, and application deadlines, require optimization for the considered problem. Combining the jointly non-dominated sorting genetic algorithm (NSGA-II) and long short-term memory (LSTM) schemes is the best way to meet the problem’s limitations. In this study, the paper designed a malware dataset with known and unknown malware. The different kinds of malware lists (e.g., cyber-attacks) are considered in the form of known and unknown malware lists with the characteristics, size of code, where malware comes from, attack on which data, and current status of the workload after being attacked by the malware. Our main idea is to present blockchain, NSGA-II, and LSTM schemes that handle phishing, routing, Sybil, and 51% of cyber-attacks without compromising application performance. Simulation results show that the study reduces delay and energy, improves accuracy, and minimizes security risks for vehicular applications.
Dual-blockchain based multi-layer grouping federated learning scheme for heterogeneous data in industrial IoT

Federated Learning (FL) allows data owners to train neural networks together without sharing local data, allowing the Industrial Internet of Things (IIoT) to share a variety of data. However, traditional federated learning frameworks suffer from data heterogeneity and outdated models. To address these issues, this paper proposed a dual-blockchain based multi-layer grouping federated learning architecture (BMFL). BMFL divides the participant groups based on the training tasks, then realizes the model training combining synchronous and asynchronous through the multi-layer grouping structure, and uses the model blockchain to record the characteristic tags of the global model, allowing group-manners to extract the model based on the feature requirements and solving the problem of data heterogeneity. In addition, to protect the privacy of the model gradient parameters and manage the key, the global model is stored in ciphertext, and the chameleon hash algorithm is used to perform the modification and management of the encrypted key on the key blockchain while keeping the block header hash unchanged. Finally, we evaluate the performance of BMFL on different public datasets and verify the practicality of the scheme with real fault dataset. The experimental results show that the proposed BMFL exhibits more stable and accurate convergence behavior than the classic FL algorithm, and the key revocation overhead time is reasonable.
GEP optimization for load balancing of virtual machines (LBVM) in cloud computing

Cloud computing relies heavily on load balancing to distribute workloads evenly among servers, network connections, and drives. The cloud system has been assigned some load which can be underloaded, overloaded, or balanced depending on the cloud architecture and user requests. An important component of task scheduling in clouds is the load balancing of workloads that may be dependent or independent of virtual machines (VMs). To overcome these drawbacks, a novel Load Balancing of Virtual Machine (LBVM) in Cloud Computing has been proposed in this paper. The input tasks from multiple users were collected in a single task collector and sent towards the load balancer, which contains the deep learning network called the Bi-LSTM technique. When the load is unbalanced, the VM migration will begin by sending the task details to the load balancer. The Bi-LSTM is optimized by a Genetic Expression Programming (GEP) optimizer and finally, it balances the input loads in VMs. The efficiency of the proposed LBVM has been determined using the existing techniques such as MVM, PLBVM, and VMIS in terms of evaluation metrics such as configuration latency, detection rate, accuracy etc. Experimental results shows that the proposed method reduces the Migration Time of 49%, 41.7%, and 17.8% than MVM, PLBVM, VMIS existing techniques respectively.
Feedback control of automatic navigation for cyborg cockroach without external motion capture system

Due to their size and locomotion ability, cockroaches are favorable as hybrid robot platforms in search and rescue (SAR) missions. However, cockroaches most likely approach the corner area and stay for an uncertain time. This natural behavior will hinder the utilization of cyborg cockroaches in SAR missions under rubble, unstructured, and unknown areas. Therefore, we proposed onboard automatic obstacle avoidance and human detection that can run on the wireless backpack stimulator without an external motion capture system. A low-power and small-size Time of Flight (ToF) sensor was selected as a distance measurement sensor, while a low-resolution thermopile array sensor was applied for human presence detection. The implemented feedback control based on IMU and ToF sensors has successfully navigated the cyborg cockroach to avoid obstacles and escape from the sharp corners in the laboratory unstructured area without stopping or being trapped. It could also recognize the human presence when the human was in front of it in real-time. Due to its performance, the random forest classifier was implemented as an embedded human detection system. It could achieve the highest accuracy at a distance of around 25 cm (92.5%) and the lowest accuracy at about 100 cm (70%).
Solving differential‐algebraic equations in power system dynamic analysis with quantum computing

Abstract Power system dynamics are generally modeled by high dimensional nonlinear differential‐algebraic equations (DAEs) given a large number of components forming the network. These DAEs' complexity can grow exponentially due to the increasing penetration of distributed energy resources, whereas their computation time becomes sensitive due to the increasing interconnection of the power grid with other energy systems. This paper demonstrates the use of quantum computing algorithms to solve DAEs for power system dynamic analysis. We leverage a symbolic programming framework to equivalently convert the power system's DAEs into ordinary differential equations (ODEs) using index reduction methods and then encode their data into qubits using amplitude encoding. The system nonlinearity is captured by Hamiltonian simulation with truncated Taylor expansion so that state variables can be updated by a quantum linear equation solver. Our results show that quantum computing can solve the power system's DAEs accurately with a computational complexity polynomial in the logarithm of the system dimension. We also illustrate the use of recent advanced tools in scientific machine learning for implementing complex computing concepts, that is, Taylor expansion, DAEs/ODEs transformation, and quantum computing solver with abstract representation for power engineering applications.
Intra-and inter-sector contextual information fusion with joint self-attention for file fragment classification

File fragment classification (FFC) aims to identify the file type of file fragments in memory sectors, which is of great importance in memory forensics and information security. Existing works focused on processing the bytes within sectors separately and ignoring contextual information between adjacent sectors. In this paper, we introduce a joint self-attention network (JSANet) for FFC to learn intra-sector local features and inter-sector contextual features. Specifically, we propose an end-to-end network with the byte, channel, and sector self-attention modules. Byte self-attention adaptively recognizes the intra-sector significant bytes, and channel self-attention re-calibrates the features between channels. Based on the insight that adjacent memory sectors are most likely to store a file fragment, sector self-attention leverages contextual information in neighboring sectors to enhance inter-sector feature representation. Extensive experiments on seven FFC benchmarks show the superiority of our method compared with state-of-the-art methods. Moreover, we construct VFF-16, a variable-length file fragment dataset to reflect file fragmentation. Integrated with sector self-attention, our method improves accuracy by more than 16.3% against the baseline on VFF-16, and the runtime achieves 5.1 s/GB with GPU acceleration. In addition, we extend our model to malware detection and show its applicability.
Quantum computing library for quantum chemistry applications

Abstract Quantum computing is aimed to solve tasks, which are believed to be exponentially hard to existing computational devices and tools. A prominent example of such classically hard problems is simulating complex quantum many-body systems, in particular, for quantum chemistry. However, solving realistic quantum chemistry problems with quantum computers encounters various difficulties, which are related, first, to limited computational capabilities of existing quantum devices and, second, to the efficiency of algorithmic approaches. In the present work, we address the algorithmic side of quantum chemistry applications by introducing a Python 3 code library, whose primary objective is to speed up the development of variational quantum algorithms for electronic structure problems. We describe the various features and capabilities of this library, including its ease in constructing customized versions of variational quantum algorithms. We elucidate how the developed library allows one to design quantum circuits and enable for the efficient execution of quantum algorithms. Furthermore, the library facilitates the integration of classical and quantum algorithms for hybrid computations and helps to realize the cross-verification of data with traditional computational methods, thereby enhancing the overall reliability of quantum chemistry simulations.
ICT-based agricultural extension literacy and the needs for agriculture development and sustainability: a case study of rice farmer in Aceh

Abstract In the current era of technological development, agricultural extension considers as one of the determinant factors for the smoothness and success of agriculture development and sustainability. This study aims to determine the literacy and the needs of lowland rice farmers towards agricultural extension using social media and its contributing factors. The study employs a questionnaire survey using simple random sampling of 48 farmers located in Kecamatan Delima, Pidie –one of the central paddy fields in the region. Data analysis used a descriptive approach and binary logistic regression analysis. The study finds that almost half of respondents had a high literacy, while 29.5 percent were moderate, and only 12.5 percent were low. Related to needs, about two-thirds of respondents stated the urgency of interest and need of using ICT-based media as a means for outreach as a medium of information, training, and satisfaction. Statistically, access to information and farmer age affect the need for information and satisfaction, while educational attributes influence the need for training. The employment of the current and latest technological media as a learning tool and diffusion of innovation is necessary for estimable and admirable alternative changing behavior in agricultural development.
Evaluating urban mini plant factories: engineering and software cost perspectives for agriculture sustainability

Abstract Precision agriculture serves as a crucial solution to address the challenges of food security and agriculture sustainability. It provides significant benefits and enables effective monitoring of agriculture throughout the entire supply chain using a technology-driven approach that prioritizes internet connectivity and leverages big data. A noteworthy application of precision agriculture is the Mini Plant Factory, where plants are cultivated in closed systems with artificial lighting. The development of the Mini Plant Factory requires an economic feasibility analysis as a determinant of technology viability before widespread implementation, aiming to promote agriculture sustainability. The Software Cost Estimation Model (SCEM) assesses software development’s cost and time requirements. Engineering economics evaluates economic feasibility using metrics like NPV , BCR , IRR , PBP , and BEP methods. Function Point Analysis for UMPF software development estimates a cost of Rp 33,052,414, equivalent to 1,311 man-hours. Investment feasibility analysis considers the utilization of UMPF as viable, with an NPV of IDR 46,938,615, BCR of 1.08, IRR of 28%, and projected profitability within four years. Precision agriculture strategically addresses complex agricultural challenges, fostering sustainability with innovations such as the UMPF . The SCEM and engineering economics confirm the feasibility and rationale for implementing UMPF technology, providing essential insights for sustainable agricultural practices.
Optimizing Echo State Networks for Continuous Gesture Recognition in Mobile Devices: A Comparative Study

Continuous gesture recognition can be used to enhance human-computer interaction. This can be accomplished by capturing human movement with the use of the Inertial Measurement Units in smartphones and using machine learning algorithms to predict the intended gestures. Echo State Networks (ESNs) consist of a fixed internal reservoir that is able to generate rich and diverse nonlinear dynamics in response to input signals that capture temporal dependencies within the signal. This makes ESNs well-suited for time series prediction tasks, such as continuous gesture recognition. However, their application has not been rigorously explored, with regard to gesture recognition. In this study, we sought to enhance the efficacy of ESN models in continuous gesture recognition by exploring diverse model structures, fine-tuning hyperparameters, and experimenting with various training approaches. We used three different training schemes that used the Leave-one-out Cross-validation (LOOCV) protocol to investigate the performance in real-world scenarios with different levels of data availability: Leaving out data from one user to use for testing (F1-score: 0.89), leaving out a fraction of data from all users to use in testing (F1-score: 0.96), and training and testing using LOOCV on a single user (F1-score: 0.99). The obtained results outperformed the Long Short-Term Memory (LSTM) performance from past research (F1-score: 0.87) while maintaining a low training time of approximately 13 seconds compared to 63 seconds for the LSTM model. Additionally, we further explored the performance of the ESN models through behaviour space analysis using memory capacity, Kernel Rank, and Generalization Rank. Our results demonstrate that ESNs can be optimized to achieve high performance on gesture recognition in mobile devices on multiple levels of data availability. These findings highlight the practical ability of ESNs to enhance human-computer interaction.
Dynamic Selection of Machine Learning Models for Time-Series Data

Adapting to changes in time-series data is a major challenge in machine learning. This problem is particularly acute in the case of limited computing power (e.g., edge devices) that does not enable the independent training of new models. While multiple studies attempt to adequately train a machine learning model on rapidly shifting data (i.e., concept drift), the challenge of dynamically selecting the most effective machine learning model for future time steps has been largely overlooked. In this study, we propose Adaptive machine learning for Dynamic ENvironments (ADEN ), a method for analyzing future trends in time-series data and selecting the most suitable ML model. Our approach models multiple aspects of the analyzed data and analyzes the behavior of multiple machine learning models on earlier time steps. By training ADEN on multiple datasets, we can deploy a zero-shot model that does not require additional training when applied to new datasets. Our evaluation, conducted on 46 time-series classification datasets, shows that ADEN not only outperforms all baselines in terms of average performance but is also capable of avoiding sudden drops in performance that characterize all other evaluated algorithms.
BPFL: Blockchain-Based Privacy-Preserving Federated Learning against Poisoning Attack

In federated learning (FL), multiple clients use local datasets to train models and submit local gradients to the server for aggregation. However, malicious clients may compromise the performance of the model by submitting poisonous gradients. Moreover, the clients do not want to reveal their training models in most application scenarios since their private data may be inferred from them. In addition, most of FL protocols lack an incentive mechanism to supervise participants and cannot punish malicious participants, which is unfair to honest participants. To tackle these problems, we propose a blockchain-based privacy-preserving federated learning against poisoning attack (BPFL). In BPFL, a blockchain-based incentive mechanism is constructed to supervise participants and promptly track malicious behaviors. BPFL can also protect the privacy of aggregated and local model if some participants are malicious, and detect poisonous data by computing cosine similarity between the aggregated gradient and local gradient of the client by using Paillier cryptosystem with threshold decryption. The experiments show that BPFL improves the accuracy of the model from 10% to 75% on CIFAR-10 against poisoning attacks, and therefore BPFL can effectively resist poisoning attacks based on the privacy of local and aggregated models.
Nonlinear circumference-based robust ellipse detection in low-SNR images

This study aims to present an effective method to detect ellipses on images with low signal-to-noise ratios (SNR). Firstly, we analyze four major interferences caused by low SNR images. A nonlinear circumference-based ellipse detection method is proposed, which uses a nonlinear circumference to vote on the parameter space constructed from input images and a spatial hierarchical search strategy to find the optimum ellipse parameters. The nonlinear circumference is designed to describe the possibility that selected edge points will generate a given ellipse. Experiments on six low SNR datasets show that our method outperforms five existing methods in terms of recall, precision and F-measure. Furthermore, a definition based on the nonlinear circumference is proposed to quantify SNR of synthetic images with ellipses. Experimental results demonstrate that our method can detect ellipses on images close to 0.2 dB while state-of-the-art methods can almost only reach 1.2 dB.
Model-agnostic Adversarial Example Detection via High-Frequency Amplification

Image classification based on Deep Neural Networks (DNNs) is vulnerable to adversarial examples, which make the classifier output incorrect predictions. One approach to defending against this attack is to detect whether the input is an adversarial example. Unfortunately, existing adversarial example detection methods heavily rely on the underlying classifier and may fail when the classifier is upgraded. In this paper, we propose a model-agnostic detection method that leverages high-frequency signals from adversarial noises in adversarial examples and does not need interactions with the underlying classifier. We amplify redundant high-frequency signals brought by adversarial noises and represent object boundaries with these signals in an image. Our key insight is that the boundaries extracted by redundant high-frequency signals have a strong correlation with the boundaries of images in adversarial examples, while this correlation does not exist in clean images. Furthermore, adversarial examples of large images have more high-frequency signals and make adversarial detection easier on large image datasets. Experimental results show that our method has good transferability and can accurately detect various adversarial examples on different datasets.
Complete multi-view subspace clustering via auto-weighted combination of visible and latent views

Multi-view subspace clustering aims to leverage the consensus and complementary information embedded within the data and effectively fuse the multiple views. However, existing methods have limitations in fully exploring the complementary information. Tensor-based methods tend to overlook the underlying structures, while latent representation-based methods struggle to capture high-order correlations, both of which are important components of complementary information. In this paper, we introduce a novel method called complete multi-view subspace clustering (CMVSC) using visible and latent views. The latent view with the underlying structures is obtained by the latent representation, along with visible views (i.e., original features) constitute the complete multi-view data. Then, the low-rank tensor constraint is imposed on the complete views to comprehensively explore the consensus and complementary information. Besides, to effectively combine the advantages of visible and latent views, we devise an auto-weighted strategy that can automatically assign the ideal weights to each view. Finally, an efficient alternating iterative algorithm using the augmented Lagrange multiplier method is designed to solve the CMVSC model. Experimental results show that our model outperforms the state-of-the-art counterparts while achieving almost perfect clustering performance on several benchmark datasets.
Multi-object tracking algorithm based on interactive attention network and adaptive trajectory reconnection

Multi-object tracking (MOT) detects multiple targets in an image and assigns a unique identifier to each target. However, challenges such as rapid motion, occlusion, and camera motion in the tracking scene may lead to identity switches (IDs) and missing trajectory problems, which degrade the performance of the tracker. To address these issues, this paper presents an MOT algorithm based on an interactive attention network and adaptive trajectory reconnection. First, an interactive attention network was created to learn the features for two different tasks of detection and tracking to alleviate feature conflicts in order to extract sufficient feature information. A new cost matrix was then designed to fuse the motion and feature information, thereby reducing the number of IDs. Meanwhile, the extreme gradient boosting reconnection module was used to achieve adaptive trajectory reconnection and reduce missing trajectories. The proposed algorithm achieved 61.5 % and 55.4 % HOTA using the standard MOT17 and MOT20 datasets, respectively. In comparison to FairMOT, our algorithm showcased notable enhancements of 3% and 1.6% on these datasets. Furthermore, when compared to state-of-the-art algorithms, the proposed algorithm demonstrated superior tracking performance.