Reviewerly/exHarmony huggingface dataset:
A Quantum-Walk-Unitary HHL Matrix Equation Solver and Its Challenges in the NISQ Era

The Harrow/Hassidim/Lloyd algorithm is a celebrated quantum matrix equation solver. Its Hamiltonian simulation involves a quantum walk process. A newly developed Quantum Walk Unitary HHL (QWU-HHL) leverages the spectral relationship between the quantum walk operator and the system matrix, and uses the quantum walk operator as its unitary in the phase estimation directly, allowing Hamiltonian simulation in the classical HHL to be removed, hence improving its efficiency. Despite the potential of being exponentially faster than classical matrix equation solvers, HHL is not feasible in the noisy intermediate-scale quantum era because its quantum circuit is too deep to preserve an accurate solution. In this work, we investigate the error behavior of QWU-HHL on a 7-qubit quantum system. Instead of executing the entire circuit, the behaviors of its sub-circuits which each contain only an initialization and a single gate are recorded. The error caused by isolated initialization and operation are analyzed.
Optimized Consensus for Blockchain in Internet of Things Networks via Reinforcement Learning

Most blockchain systems currently adopt resource-consuming protocols to achieve consensus between miners; for example, the Proof-of-Work (PoW) and Practical Byzantine Fault Tolerant (PBFT) schemes, which have a high consumption of computing/communication resources and usually require reliable communications with bounded delay. However, these protocols may be unsuitable for Internet of Things (IoT) networks because the IoT devices are usually lightweight, battery-operated, and deployed in an unreliable wireless environment. Therefore, this paper studies an efficient consensus protocol for blockchain in IoT networks via reinforcement learning. Specifically, the consensus protocol in this work is designed on the basis of the Proof-of-Communication (PoC) scheme directly in a single-hop wireless network with unreliable communications. A distributed MultiAgent Reinforcement Learning (MARL) algorithm is proposed to improve the efficiency and fairness of consensus for miners in the blockchain system. In this algorithm, each agent uses a matrix to depict the efficiency and fairness of the recent consensus and tunes its actions and rewards carefully in an actor-critic framework to seek effective performance. Empirical results from the simulation show that the fairness of consensus in the proposed algorithm is guaranteed, and the efficiency nearly reaches a centralized optimal solution.
Vector-valued Fourth-order Reaction-diffusion based Photon-limited Multi-channel Image Filtering

A nonlinear partial differential equation (PDE)- based photon-limited multi-channel image restoration technique is proposed here. It uses a novel well-posed vector-valued fourth-order reaction-diffusion model. The considered model leads to an anisotropic diffusion equation system whose single-valued PDEs are correlated by some shared terms. A finite difference-based discretization algorithm that solves numerically this reaction-diffusion system is then proposed. This fast-converging iterative numerical approximation scheme has been used successfully in the quantum denoising tests which are finally discussed in this paper.
Skeleton-based driver action recognition using ResGGCNN

Driver action recognition serves a crucial role in assessing a driver’s distraction and readiness to takeover control in Level 3 of autonomy. In this study, we proposed an effective, driver-independent graph convolutional neural network (GCNN) using the skeletal representation by comprising determinative joints and inter-joint distances in the driver’s body structure. The proposed technique eliminates superfluous information. To distinguish various action classes, our proposed GCNN leverages residual gated connections (ResGGCNN), which enables the model to learn features efficiently at multiple levels. The proposed ResGGCNN is superbly lightweight with only almost 110k learnable parameters, makes it adequate for low-latency and embedded deployment. We evaluated our proposed model on the 3MDAD dataset with 16 common driving and non-driving related activities and achieved significant improvement in the recognition accuracy of driver’s action at the rate of 59.71% on RGB front-view modality.
Counterfactual Attention for Facial Image Super-Resolution

Super-resolution (SR) is the task of recovering High-Resolution (HR) images from given Low- Resolution (LR) Images. Various SR methods are available in the literature. The attention mechanism is one of the widely used approaches in the field of SR. In this paper, Counterfactual Attention Learning (CAL) based on causal inference is applied to increase the quality of attention in Face Super-Resolution (FSR). This approach helps to assess the quality of attention and provides a strong signal to supervise the learning activity. The paper discusses the effect of the learned attention on the task of SR through counterfactual intervention and the effect is maximized to make the model learn useful attention for FSR. The effectiveness of the method is tested, and upscaling is achieved using the Scale-Arbitrary SR model (ArbSR), which can handle both integer and non-integer scale factors. The experiments are carried out for different scale factors on the CelebA dataset. The results show that the technique enhances the performance of FSR task by both quality of the image and PSNR.
On the FPGA Implementation of Capsule Networks

The paper presents the conceptual modules required by the FPGA implementation of a capsule network and their interconnected arrangement using a capsule network designed and trained for classifying digitally modulated signals. The FPGA implementation requires firmware written using a Hardware Descriptive Language (HDL) such as Verilog or VHDL, and can be obtained by writing custom HDL modules, using Vivado tools to generate HDL from C code, or by employing MATLAB tools to generate HDL from MATLAB scripts.
RS-MCQA: Multi-class Question Aware Visual Question Answering for Optical Remote Sensing Datasets

We tackle the problem of visual question answering (VQA) towards automated landcover information retrieval. Analysis of the remote sensing datasets involves simple classification problems to complex regression challenges of the widely diversified ground objects with varying shapes and sizes. Prior research carries low contextual information of the questions with the remotely sensed object images, impacting the generalization performance. This paper proposes a Global Residual DenseNet to exploit image feature reuse with a controlled growth rate in the channel dimension of convolution filters. Further, image and question embedding are stacked in interleave for a resonant coupling. Finally, we optimize multi-class question-aware decoders (RS-MCQA) using multi-class margin and binary cross-entropy loss for learning a robust semantic correlation between the image and the generic land-cover analysis questionaries. Experimental results on the benchmark RSVQA and RSIVQA datasets show the proposed method’s state-of-the-art performance in the context of remote sensing VQA task.
Convolutional Neural Network implemented on FPGA for trajectory classification

Artificial intelligence (AI) models are popular nowadays due to their capacity to provide accurate classification results. A hardware implementation of an AI model offers major benefits such as deterministic and predictable functioning, low power consumption and the possibility of embedding the AI model in a mobile application. In this paper we present the hardware implementation of a Convolutional Neural Network (CNN) on a Xilinx Field Programmable Gate Array (FPGA) device. Our implementation is compact (small area) being aimed at very low power consumption. We describe the architecture and the choices we made for the parallel implementation. We prove the usefulness of our approach for simple trajectory classification. We conclude that our hardware implementation leads to interesting experimental results in terms of resource utilization, power consumption and running speed.
Mitigate Gender Bias Using Negative Multi-task Learning

Deep learning models have showcased remarkable performances in natural language processing tasks. While much attention has been paid to improvements in utility, privacy leakage and social bias are two major concerns arising in trained models. In this paper, we address both privacy protection and gender bias mitigation in classification models simultaneously. We first introduce a selective privacy-preserving method that obscures individuals’ sensitive information by adding noise to word embeddings. Then, we propose a negative multi-task learning framework to mitigate gender bias, which involves a main task and a gender prediction task. The main task employs a positive loss constraint for utility assurance, while the gender prediction task utilizes a negative loss constraint to remove gender-specific features. We have analyzed four existing word embeddings and evaluated them for sentiment analysis and medical text classification tasks within the proposed negative multi-task learning framework. For instances, RoBERTa achieves the best performance with an average accuracy of 95% for both negative and positive sentiment, with 1.1 disparity score and 1.6 disparity score respectively, and GloVe achieves the best average accuracy of 96.42% with a 0.28 disparity score for the medical task. Our experimental results indicate that our negative multi-task learning framework can effectively mitigate gender bias while maintaining model utility for both sentiment analysis and medical text classification.
ECONOMIZATION IN INFORMAL ELECTRONICALLY MEDIATED COMMUNICATION: ELLIPSES AND SENTENTIAL ALPHABETISMS

The paper looks at the phenomenon of sentential alphabetisms and the combination of economization processes applied to their production. The term alphabetism is used as a broad category to cover acronyms and initialisms which both result from acronymization processes. The focus is on sentential alphabetisms and their unacronymized equivalents with the aim to verify the assumption that economization in a significant number of cases involves a combination of processes at the lexical level (acronymization) and the syntactic level (syntactic ellipsis).
Research on underwater degraded image enhancement technology based on lightweight network

The underwater imaging environment is relatively complex. The light in the water has blurred image edges, low overall contrast, and the objects in the water are difficult to recognize. This paper proposes a method based on depth learning and physical model to enhance the quality of underwater image. The enhanced image is obtained by building the module to calculate the important parameters of the model combined with the input. The experimental results show that the algorithm in this paper has faster processing speed and better imaging effect than traditional methods, and can correct the color deviation of underwater images.
Prediction of Wordle player data based on BP neural network optimized by genetic algorithm

This article mainly uses the data provided by Wordle player to predict the distribution of score results for a certain word in the future. However, the scoring results have high uncertainty. There are many evaluation indicators and impact factors. In order to predict the results more accurately, this paper proposes a BP neural network data prediction model based on genetic algorithm optimization(GA BP algorithm). We introduce three factors: time, prevalence of each word and number of repeated letters. We determine the BP neural network topology, each parameter of BP and each parameter of GA, then train it. The data correlation between the training set, validation set, test set and overall results after training is derived and the result is good. The predicted result for the term EERIE on March 1, 2023 is: 0.00%, 2.06%, 16.21%, 32.95%, 28.50%, 15.78%, 4.48% from 1 to X in that order.
Research on the architecture of modern radar support information management system

In recent years, our army has attached great importance to the development and use of equipment support information systems. In the field of radar equipment support, some equipment information support subsystems have been successfully developed. However, there is still a lack of systematic research on a series of issues such as what content, functions, and elements should be included in the radar integrated support information system, and how various subsystems should coordinate their work. In view of this, firstly, the functional requirements of the radar integrated support system are analyzed. On this basis, the architecture of the radar integrated support information system is proposed, and the structures and functions of three main subsystems, namely, integrated management, integrated support network database, and simulation training, are described in detail. Finally, the engineering implementation of the information system is further elaborated.
Strategy decision on cycling race based on genetic algorithm

This paper focuses on the individual time trial and has a study of the strategy selection for the male Sprinter and Time Specialist. Firstly, the general characteristics of the sprinters and time specialists are given according to 4 abilities. And the terrains of the track are defined by the curvature and slope. Also, the strategies of the riders are classified into dashing, speeding up, keeping a constant velocity, and slowing down. Then, the power-consumed model is established. We choose the course of 2021 Olympic Time Trial course in Tokyo as an example and get the data of the track. To get the velocity of the riders at every step, Newton Iterative Method is used. And Genetic Algorithm is applied to get the optimal strategy selection for the riders to take the shortest time. To validate the model, the time the champion, who is a sprinter, takes is compared to this example, which proves the reliability of the model. Finally, the results show that the sprinters should apply less power on the downhill road to storage more power for the uphill, and time specialists should spend less power on the uphill and recover the energy in time to prepare for next explosiveness. And they both need to dash at the end
Improved particle swarm optimization based on hyperbolic cross points algorithm

Particle swarm optimization (PSO) has many advantages, such as swarm intelligence, intrinsic concurrency, simple iteration format, and fast convergence speed, so it has attracted much research interest. An improved standard particle swarm optimization based on hyperbolic cross points algorithm (HCPA-SPSO) has good global and local search capability, which can significantly improve the search accuracy and success rate of SPSO. First, a particle initialization strategy is proposed to generate the initial particle swarm by global HCPA. Second, HCPA is introduced as a local evolution operator. The fuzzy C-means clustering method is used to classify the particle swarm at each fixed iteration step, and a local HCPA search for the representative particles in each class is performed. Finally, the proposed algorithm is compared with SPSO at typical test functions. For the objective function of Multi-local minima-shaped, Bowl-shaped and Valley-shaped, HCPASPSO can significantly improve the success rate and accuracy of the optimal solution. For the Plate-shaped objective function, HCPA-SPSO can further improve the success rate and the accuracy of the optimal solution. The accuracy of the proposed algorithm is similar to that of SPSO for the Steep ridges-shaped objective function whose optimal solution is infinite non-repeating decimal. By combining the global search capability of SPSO and the local search capability of HCPA, the proposed algorithm can effectively balance the exploration and development capability of the whole algorithm. The research results can provide an optimization algorithm with higher accuracy for optimal design and fault diagnosis.
Pure azimuth passive positioning of UAV based on iterative algorithm and triangular relationship

In recent years, UAVs have been widely used, especially the demand for cooperative formation of multiple UAV clusters is increasing. Therefore, how to move the shortest distance to make the UAV formation reach the target formation has become a problem worthy of study. Taking the formation of ten UAVs as an example, this paper discusses how to move UAVs to form a circular target formation through the shortest distance. First, the shortest distance model based on the binary iterative algorithm is established to solve the polar coordinates of the target position of the UAV, and then the pure azimuth passive positioning model is established using the triangular relationship. This method can realize the effective positioning of UAVs in different situations, make UAVs maintain stable formation when flying in cluster formation, and can be extended to deal with the formation of UAVs with different numbers and shapes.
Research on word classification based on logistic regression and machine learning

Anagram is a popular daily puzzle offered by The New York Times. Players solve the puzzle by guessing a five-letter word in six or fewer attempts, with feedback for each guess. For this version, each guess must be an actual English word. The main purpose of this paper is to build a model to predict the future report results by taking the daily change in the number of report results, the change in date and the classification of difficulty as variables and taking "eerie" as an example. Classification of word difficulty is a problem of five inputs and seven outputs. In this paper, RSR model is established. Firstly, entropy weight method is used to evaluate 1try,2tries, 3tries,4tries, 5tries, 6tries. 7or more tries (X) to calculate the difficulty level of index weight division. Rank indexes from small to large, calculate the rank sum ratio, and finally make statistical regression ranking.
Research on ship identification based on VGG network and millimeter wave radar

Aiming at the problem that the rate of ship identification is still low, a method of ship identification on river surface is proposed. In this paper, MATLAB software is first used to simulate the IF signal generated by millimeter-wave radar. Then, by setting the reflection coefficient of steel plate (ship), water (water surface), sand (river bank) and the relative distance of radar, the simulation experiment is carried out to obtain the generated time domain diagram and energy diagram. Then, the Fourier transform of the signal is carried out by MATLAB software to obtain its range spectrum diagram. Then the obtained time domain diagram, energy diagram and frequency domain diagram are processed through a series of images and input into the neural network for classification learning. Then, the experimental test was carried out through the millimeter wave radar signal acquisition plate with one round and four rounds of collection. By collecting the characteristic spectra of steel plate, sand and water at different distances and then classifying them, it was found that the recognition accuracy in the energy and time domain could reach 95%, effectively classifying and recognizing the three substances: steel plate (ship), water (water surface) and sand (river bank).
Dense pedestrian detection algorithm based on improved Yolov5-DCN

Paper investigates an enhanced approach on YOLOv5 method that solves the challenges posed by complex environmental backgrounds, intensive repeated target detection, and varying pedestrian sizes. In the backbone network, feature extraction mainly depends on the texture information and shape of the target. We use deformable convolutional network (DCN) to replace the traditional convolution.;In Non-Maximum Suppression(NMS),The Generalized Intersection over Union(GIOU) used by original network is in place of through Distance Intersection over Union Loss(DIOU) solve problem of the densely populated repeated test.We use crowdhuman training data set to evaluate the effectiveness of the algorithm.We observed that the detection accuracy of the improved Yolov5-DCN model was 83.8%, which was 1.6% higher than that of the basic model. Moreover, it can effectively improve the accuracy of pedestrian target detection in dense scenes, especially for the detection of dense occluded targets, and the effect is significantly improved.
Overview of Amazon chess’ game playing algorithms

Amazon chess is a machine game with high dimension branches, in which the first step is more than 2000 actions, so it is an ideal test platform for search algorithm and Monte Carlo tree search. This paper analyses the rules and common game algorithms of Amazon chess based on its environment, and compares the advantages and disadvantages of different algorithms. Finally, the improvement direction of Monte Carlo tree search algorithm is reviewed from four aspects of evaluation function, database, search strategy and parallel computing, and the research prospect of Amazon Monte Carlo game algorithm is prospected
Design of underground multi-mode remote transmission sub system

Downhole TV plays an important role in oil exploitation, among which it is a key part of downhole TV. At present, most of the downhole TV industry uses armored cable for data transmission, but with the development of technology, it has begun to be equipped with optical fiber in the field working environment, and continuous tubing also began to be equipped with optical fibers. Based on this situation, this paper introduces a system design of photoelectric multi-mode remote transmission. This paper first introduces the principle of data coding and demodulation. After analyzing the characteristics of data transmission, TC-PAM coding modulation mode is adopted, and during the cable transmission, the cable has adaptive technology to improve the decoding accuracy of the decoding terminal during high-speed data transmission in the deep well test channel environment. Then, the working principle and system composition of teletransmission section are introduced, and the design and system design requirements of photoelectric composite cable, the selection of core modules and the design method of temperature and pressure resistance of the system are also given. Finally, the field environment test verifies the stable operation of the system and the feasibility of the design method.