Reviewerly/exHarmony huggingface dataset:
Multi-Key Homomorphic Encryption Scheme with Multi-Output Programmable Bootstrapping

Multi-key Homomorphic Encryption (MKHE) scheme can homomorphically evaluate ciphertexts encrypted by different keys, which can effectively protect the privacy information of data holders in the joint computing of cloud services. Since the first full Homomorphic encryption scheme was proposed, bootstrapping is the only way to realize the arbitrary depth homomorphic computation of MKHE schemes. But bootstrap operation is quite expensive. In order to implement fast bootstrapping in MKHE schemes, previous works proposed multi-key TFHE schemes to implement low-latency bootstrapping and output a univariate function of messages after bootstrapping, called Programmable Bootstrapping (PBS). However, these schemes can only encrypt single-bit messages. PBS only outputs a function. And after a homomorphic operation, a bootstrap is required, which undoubtedly results in an increase in the cost of the whole multi-key homomorphic encryption operation. In this paper, we propose a MKHE scheme for multi-output PBS. For this purpose, we study the encryption method and homomorphic operation steps of MKHE, and add BFV homomorphic encryption multiplication and multi-key ciphertext relinearization. We separate the homomorphic operation from bootstrapping. We homomorphically evaluate test polynomials for multiple functions. In contrast to previous MKHE schemes, we support the output of multiple message-related functions with a single bootstrapping operation on the ciphertext. It is no longer limited to encrypting single-bit plaintext, and an effective ciphertext packaging technology is added. According to the analysis given in this paper, it is known that in the scenario of multi-party joint computation, the proposed scheme can be implemented with less bootstrapping when the same number of functions are homomorphically operated. This will effectively reduce the computational overhead.
A Maximally Split and Adaptive Relaxed Alternating Direction Method of Multipliers for Regularized Extreme Learning Machines

One of the significant features of extreme learning machines (ELMs) is their fast convergence. However, in the big data environment, the ELM based on the Moore–Penrose matrix inverse still suffers from excessive calculation loads. Leveraging the decomposability of the alternating direction method of multipliers (ADMM), a convex model-fitting problem can be split into a set of sub-problems which can be executed in parallel. Using a maximally splitting technique and a relaxation technique, the sub-problems can be split into multiple univariate sub-problems. On this basis, we propose an adaptive parameter selection method that automatically tunes the key algorithm parameters during training. To confirm the effectiveness of this algorithm, experiments are conducted on eight classification datasets. We have verified the effectiveness of this algorithm in terms of the number of iterations, computation time, and acceleration ratios. The results show that the method proposed by this paper can greatly improve the speed of data processing while increasing the parallelism.
Underwater Image Color Constancy Calculation with Optimized Deep Extreme Learning Machine Based on Improved Arithmetic Optimization Algorithm

To overcome the challenges posed by the underwater environment and restore the true colors of marine objects’ surfaces, a novel underwater image illumination estimation model, termed the iterative chaotic improved arithmetic optimization algorithm for deep extreme learning machines (IAOA-DELM), is proposed. In this study, the gray edge framework is utilized to extract color features from underwater images, which are employed as input vectors. To address the issue of unstable prediction results caused by the random selection of parameters in DELM, the arithmetic optimization algorithm (AOA) is integrated, and the search segment mapping method is optimized by using hidden layer biases and input layer weights. Furthermore, an iterative chaotic mapping initialization strategy is incorporated to provide AOA with a better initial search proxy. The IAOA-DELM model computes illumination information based on the input color vectors. Experimental evaluations conducted on actual underwater images demonstrate that the proposed IAOA-DELM illumination correction model achieves an accuracy of 96.07%. When compared to the ORELM, ELM, RVFL, and BP models, the IAOA-DELM model exhibits improvements of 6.96%, 7.54%, 8.00%, and 8.89%, respectively, making it the most effective among the compared illumination correction models.
Correction of Threshold Determination in Rapid-Guessing Behaviour Detection

Assessment is one benchmark in measuring students’ abilities. However, assessment results cannot necessarily be trusted, because students sometimes cheat or even guess in answering the questions. Therefore, to obtain valid results, it is necessary to separate valid and invalid answers by considering rapid-guessing behaviour. We conducted a test to record exam log data from undergraduate and postgraduate students to model rapid-guessing behaviour by determining the threshold response time. Rapid-guessing behaviour detection is inspired by the common k-second method. However, the method flattens the application of the threshold, thus allowing misclassification. The modified method considers item difficulty in determining the threshold. The evaluation results show that the system can identify students’ rapid-guessing behaviour with a success rate of 71%, which is superior to the previous method. We also analysed various aggregation techniques of response time and compared them to see the effect of selecting the aggregation technique.
Ensemble Learning Method Using Stacking with Base Learner, A Comparison

Stacking is a novel broad technique to meta-learning. Stacking is a technique for combining the predictions of several heterogeneous categorization models (Liu et al. in A stacked generalization ensemble model for optimization and prediction of the gas well rate of penetration: a case study in Xinjiang, 2021). Stacking is used to achieve universal accuracy. Unlike other methods of combining, (e.g., boosting, bagging) (Dietterich in Multiple Classifier Systems, Springer, Berlin, Heidelberg, 2000), in stacking, when the original training data has been transformed into numerous subsets known as bootstraps, we send the entire dataset to base learners. How to use stacking to enhance a single generalizer is also covered in detail. Performance has increased, according to two trials. As a level-1 generalizer, HERBIE's metrics are utilized in the first experiment with a single generalizer. The process of stacking entails fitting multiple distinct kinds of models on the same data and uses another model to learn how to integrate the predictions in the most effective way possible.
Tracking aftershock sequences using empirical matched field processing

SUMMARY Extensive aftershock sequences present a significant problem to seismological data centres attempting to produce near real-time comprehensive seismic event bulletins. An elevated number of events to process and poorer performance of automatic phase association algorithms can lead to large delays in processing and a greatly increased human workload. Global monitoring is often performed using seismic array stations at considerable distances from the events involved. Empirical matched field processing (EMFP) is a narrow-frequency band array signal processing technique that recognizes the inter-sensor phase and amplitude relations associated with wavefronts approaching a sensor array from a given direction. We demonstrate that EMFP, using a template obtained from the first P arrival from the main shock alone, can efficiently detect and identify P arrivals on that array from subsequent events in the aftershock zone with exceptionally few false alarms (signals from other sources). The empirical wavefield template encodes all the narrow-band phase and amplitude relations observed for the main shock signal. These relations are also often robust and repeatable characteristics of signals from nearby events. The EMFP detection statistic compares the phase and amplitude relations at a given time in the incoming data stream with those for the template and is sensitive to very short-duration signals with the required characteristics. Significant deviations from the plane wavefront model that typically degrade the performance of standard beamforming techniques can enhance signal characterization using EMFP. Waveform correlation techniques typically perform poorly for aftershocks from large earthquakes due to the distances between hypocentres and the wide range of event magnitudes and source mechanisms. EMFP on remote seismic arrays mitigates these difficulties; the narrow-band nature of the procedure makes arrival identification less sensitive to the signals’ temporal form and spectral content. The empirical steering vectors derived for the main shock P arrival can reduce the frequency dependency of the slowness vector estimates. This property helps us to automatically screen out arrivals from outside of the aftershock zone. Standard array processing pipelines could be enhanced by including both plane-wave and empirical matched field steering vectors. This would maintain present capability for the plane-wave steering vectors and provide increased sensitivity and resolution for those sources for which we have empirical calibrations.
SP-YOLO-Lite: A Lightweight Violation Detection Algorithm Based on SP Attention Mechanism

In the operation site of power grid construction, it is crucial to comprehensively and efficiently detect violations of regulations for the personal safety of the workers with a safety monitoring system based on object detection technology. However, common general-purpose object detection algorithms are difficult to deploy on low-computational-power embedded platforms situated at the edge due to their high model complexity. These algorithms suffer from drawbacks such as low operational efficiency, slow detection speed, and high energy consumption. To address this issue, a lightweight violation detection algorithm based on the SP (Segmentation-and-Product) attention mechanism, named SP-YOLO-Lite, is proposed to improve the YOLOv5s detection algorithm and achieve low-cost deployment and efficient operation of object detection algorithms on low-computational-power monitoring platforms. First, to address the issue of excessive complexity in backbone networks built with conventional convolutional modules, a Lightweight Convolutional Block was employed to construct the backbone network, significantly reducing computational and parameter costs while maintaining high detection model accuracy. Second, in response to the problem of existing attention mechanisms overlooking spatial local information, we introduced an image segmentation operation and proposed a novel attention mechanism called Segmentation-and-Product (SP) attention. It enables the model to effectively capture local informative features of the image, thereby enhancing model accuracy. Furthermore, a Neck network that is both lightweight and feature-rich is proposed by introducing Depthwise Separable Convolution and Segmentation-and-Product attention module to Path Aggregation Network, thus addressing the issue of high computation and parameter volume in the Neck network of YOLOv5s. Experimental results show that compared with the baseline network YOLOv5s, the proposed SP-YOLO-Lite model reduces the computation and parameter volume by approximately 70%, achieving similar detection accuracy on both the VOC dataset and our self-built SMPC dataset.
Need of Hour: Hybrid Encryption and Decryption Standards (HEaDS) Algorithm for Data Security

With the significant advancements in information technology, a massive amount of data is being shared over the internet. And over the previous few years, one persistent obstacle has proven to be one of the significant challenges, namely: data security. Data must be secured so that electronic data transfer over the internet can be conducted safely and swiftly. Encryption algorithms ensure the security of data transmitted over the internet. In this paper, we propose a novel cryptographic algorithm, Hybrid Encryption and Decryption Standards (HEaDS), which is originated after the combination of Stream Cipher and Substitution Cipher algorithms. Also, we have analyzed the Hybrid Encryption and Decryption Standards (HEaDS) algorithm with the current AES algorithm and DES algorithm to check the robustness of data security.
GPStar4: A flexible framework for experimenting with genetic programming

GPStar4 is a flexible workbench for experimenting with population algorithms. It is a framework that defines a genetic cycle, with inflection points for implementing an algorithm's specific behaviors; it also provides a variety of implementations for these inflection points. A user of the system can select from the provided implementations and customize the places where alternative behavior is desired, or even create their own implementations. Components interact through a context mechanism that enables both mutable and immutable information sharing, type checking, computed defaults and event listeners.
Adaptive Fuzzy Inference Decision Strategy for Service Robots Tidying Up Objects

Tidying up objects poses a complex challenge for service robots, particularly when it involves scheduling operations that require real-time action for achieving optimal results. This paper presents a decision system aimed at selecting priority locations to address the problem of robots searching for miscellaneous items in indoor environments. Initially, three datasets with varying complexities are generated using real environmental data, and the environmental information is integrated into feature matrices. Subsequently, the rule base of the fuzzy inference system is trained and optimized using an equilibrium optimizer, and its performance is compared with other commonly used algorithms. The feature matrices and the optimized rule base are then incorporated into the fuzzy inference system, leveraging the traveling salesman problem to determine the optimal sequence for visiting locations. The accuracy and efficiency of the proposed method are validated through physical implementation tests using analog data. Finally, the system is applied and evaluated in real-world scenarios to validate its effectiveness. The video of experiment is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://youtu.be/rjMDgopM-9M</uri> .
Maximizing the Potential of Artificial Intelligence in Digital Marketing

The use of artificial intelligence (AI) in digital marketing has become increasingly prevalent in recent years, with businesses seeking to maximize the potential of this technology to improve customer engagement and drive sales. However, despite the promise of AI, many organizations struggle to fully leverage its capabilities in their digital marketing efforts. This study aims to address this gap by exploring how businesses can maximize the potential of AI in digital marketing. Through a comprehensive review of the literature and empirical research, the authors identify the key challenges and opportunities associated with the use of AI in digital marketing and provide practical recommendations for businesses seeking to optimize their AI strategies. The study highlights the potential benefits of using AI in digital marketing, including the ability to personalize customer experiences, optimize advertising campaigns, and improve customer engagement. However, several challenges are associated with the use of AI, such as data privacy concerns and the potential for bias in algorithms. To address these challenges, the study provides practical recommendations for businesses seeking to maximize the potential of AI in their digital marketing efforts. These include developing a clear AI strategy, investing in high-quality data, and ensuring that AI models are transparent and explainable. Overall, this manuscript provides a valuable contribution to the field of digital marketing by demonstrating how businesses can leverage the power of AI to improve customer engagement and drive sales. By following the practical recommendations outlined in this study, businesses can position themselves for success in the rapidly evolving world of digital marketing.
BIFT: A federated learning System for Connected and Autonomous Vehicles Based on Blockchain

Algorithms for machine learning(ML) are essential for autonomous driving. The vast majority of history’s automated and connected cars send a significant quantity of movement data collected from many automobiles to a central server for regular training. Nonetheless, the info process has made the safety and confidentiality of information essential. Nowadays, the development of federated learning (FL) for data security has the potential to potential to enhance distributed machine learning’s data privacy. The malicious attackers might still target the training process, though. FL is extremely vulnerable because of its total dependency on the central server. We suggest BIFT as a solution to the aforementioned issue, which consists of two parts: 1) a completely decentralized 2) A blockchain-based machine learning system for CAVs that guarantees privacy. With the use of their own driving data, BIFT’s scattered CAVs may train local machine learning models, which they can subsequently upload to improve the global model. In order to fend off potential attackers, BIFT offers a consensus mechanism called Proof of Federated Learning. We assess BIFT’s performance and demonstrate that it is scalable, reliable, and capable of fending off harmful assaults.
Robust facial expression recognition with Transformer Block Enhancement Module

Recently, facial expression recognition (FER) methods have achieved significant progress. However, FER is still challenged by factors such as uneven illumination and low-quality expression images. Exploring the potential of facial expression features to achieve robust FER is particularly important. Inspired by the Transformer’s excellent performance in modeling long-range dependencies and in vision tasks, this paper proposes a Transformer Block Enhancement Module (TBEM) for enhancing the feature representation of facial expressions. The proposed module contains a Channel Enhancement (CE) block and a Spatial Enhancement (SE) block. The CE block can adaptively enhance the expression features on the channel dimension by effectively leveraging the channel dependency information, while the SE block enhances the expression features on the spatial dimension by integrating the spatial dependency information. TBEM can output a more robust expression representation by combining CE and SE. The proposed artificial intelligence learning module greatly improves the recognition accuracy of FER engineering tasks. To further illustrate the application of TBEM in real-world FER engineering, three engineering problems are used for verification. Extensive experiments demonstrate that the proposed method improves FER performance by focusing on more accurate decisional features and can be easily embedded into regular convolutional neural network models to help them improve the accuracy on FER tasks by about 2.64%–3.03%. The results show the proposed method achieves accuracies of 90.57% on FERPlus, 89.41% on RAFDB basic and 68.43% on RAFDB compound, respectively. The proposed method also provides a meaningful reference for further research on applying Transformer to other tasks.
Pianissimo: A Sub-mW Class DNN Accelerator with Progressive Bit-by-Bit Datapath Architecture for Adaptive Inference at Edge

Pianissimo is a sub-mW class inference accelerator that adaptively responds to the changing edge environmental conditions with a progressive bit-by-bit datapath architecture. SWHW cooperative control with the custom RISC and the HW counters allows Pianissimo adaptive/mixed precision and block skip, providing a better accuracy-computation tradeoff for low-power edge AI. The 40 nm chip, with 1104 KB memory, dissipates 793-1032$\mu$W at 0.7 V on MobileNetVl, achieving 0. 49-1.25TOPS/W at this ultra-low power range.
Color Image Encryption Algorithm Based on Cross-Spiral Transformation and Zone Diffusion

Due to their rich information, color images are frequently utilized in many different industries, but the network’s security in handling their delivery of images must be taken into account. To improve the security and efficiency of color images, this paper proposed a color image encryption algorithm based on cross-spiral transformation and zone diffusion. The proposed algorithm is based on Chen’s system and the piecewise linear chaotic map, and uses the chaotic sequences generated by them for related operations. Firstly, the R, G and B planes are extracted, and the spiral starting point of each plane is randomly selected by the chaotic sequence to implement the cross-spiral transformation. Secondly, the bit-level image matrix is constructed by the scrambled image matrix, and the bit-level chaotic matrix is constructed by the chaotic sequence. Finally, the three-dimensional matrix is divided into four zones by a dividing line, and partition diffusion is carried out to obtain the encrypted image. Simulation results and algorithm analyses indicate that the proposed algorithm has superior performance and can resist a wide range of attacks.