Reviewerly/exHarmony huggingface dataset:
Single-objective and multi-objective optimization for variance counterbalancing in stochastic learning

Artificial neural networks have proved to be useful in a host of demanding applications, therefore becoming increasingly important in science and engineering. Large-scale problems constitute a challenging task for training neural networks using the stochastic gradient descent method and variations, which are based on the random selection of mini-batches of training points at every iteration. The challenge lies on the mandatory use of diminishing search step sizes in order to retain mild error fluctuations throughout the training set preserving so the quality of the network’s generalization capability. Variance counterbalancing was recently proposed as a remedy for addressing the diminishing step sizes in neural network training using stochastic gradient methods. It is based on the concurrent minimization of the average mean squared error of the network along with the error variance over random sets of mini-batches. Also, it promotes the use of advanced optimization algorithms instead of the slowly convergent gradient descent. The present work aims at enriching our understanding of the original variance counterbalancing approach, as well as reformulating it as a multi-objective problem by taking advantage of its bi-objective nature. Experimental analysis reveals the performances of the studied approaches and their competitive edge over the established Adam method.
Automatic Keyword Extraction Algorithm for Chinese Text based on Word Clustering

There are some problems in automatic keyword extraction of Chinese text, such as large feature extraction error, low precision of extracted keywords, and poor real-time performance. Therefore, an automatic keyword extraction algorithm for Chinese text based on word clustering is designed. Calculate keyword frequency, document frequency and inverse document frequency features through statistical algorithm, measure the degree of interdependence between keywords with the help of point mutual information, and construct keyword feature item quantification matrix with the help of vector space model corresponding to keywords and feature items to complete keyword feature quantification and realize keyword feature extraction of Chinese text. Calculate the average semantic similarity of keyword words, determine the similarity of keyword features, and eliminate the keyword features with high similarity; Set the comprehensive feature value of the importance of single word words in Chinese text, determine the importance of single word words in the text, remove the single word words with low importance, and use Bayesian framework to reduce the dimension of high-dimensional keyword feature data to realize preprocessing research. The mapping results of keyword vector space model are determined by word clustering algorithm, the text clusters of keyword space clustering results are calculated by clustering algorithm, and the keywords are classified by DBN method. On this basis, the automatic keyword extraction model of Chinese text is designed to realize the automatic keyword extraction of Chinese text. The experimental results show that the design algorithm can effectively reduce the feature extraction error and improve the extraction efficiency.
Time-series benchmarks based on frequency features for fair comparative evaluation

Time-series prediction and imputation receive lots of attention in academic and industrial areas. Machine learning methods have been developed for specific time-series scenarios; however, it is difficult to evaluate the effectiveness of a certain method on other new cases. In the perspective of frequency features, a comprehensive benchmark for time-series prediction is designed for fair evaluation. A prediction problem generation process, composed of the finite impulse response filter-based approach and problem setting module, is adopted to generate the NCAA2022 dataset, which includes 16 prediction problems. To reduce the computational burden, the filter parameters matrix is divided into sub-matrices. The discrete Fourier transform is introduced to analyze the frequency distribution of transformed results. In addition, a baseline experiment further reflects the benchmarking capability of NCAA2022 dataset.
The Role of Augmented Reality in the Advancement of Minimally Invasive Surgery Procedures: A Scoping Review

The purpose of this review was to analyze the evidence on the role of augmented reality (AR) in the improvement of minimally invasive surgical (MIS) procedures. A scoping literature search of the PubMed and ScienceDirect databases was performed to identify articles published in the last five years that addressed the direct impact of AR technology on MIS procedures or that addressed an area of education or clinical care that could potentially be used for MIS development. A total of 359 studies were screened and 31 articles were reviewed in depth and categorized into three main groups: Navigation, education and training, and user-environment interfaces. A comparison of studies within the different application groups showed that AR technology can be useful in various disciplines to advance the development of MIS. Although AR-guided navigation systems do not yet offer a precision advantage, benefits include improved ergonomics and visualization, as well as reduced surgical time and blood loss. Benefits can also be seen in improved education and training conditions and improved user-environment interfaces that can indirectly influence MIS procedures. However, there are still technical challenges that need to be addressed to demonstrate added value to patient care and should be evaluated in clinical trials with sufficient patient numbers or even in systematic reviews or meta-analyses.
Measurement of the Temperature Dependence of Polarization Switching in Gain-Switched VCSELs for Quantum Random Number Generation

We report an experimental study of the effect of the temperature of the VCSEL on the probability of excitation of a linearly polarized mode when gain-switching the device. We consider different modulation frequencies and amplitudes. We show that the probability of excitation of a linearly polarized mode significantly changes with the value of the temperature of the device. We also show that for low values of the temperature the probability of excitation saturates to a constant value as the amplitude of the modulation increases. This extends our previous results obtained at larger temperatures for which that saturation was not observed. We identify situations in which the distributions of the linearly polarized signals at a sampling time are approximately uniform. For these cases we evaluate the quality of the random numbers by using statistical test.
A Preliminary Study of Deep Learning Sensor Fusion for Pedestrian Detection

Most pedestrian detection methods focus on bounding boxes based on fusing RGB with lidar. These methods do not relate to how the human eye perceives objects in the real world. Furthermore, lidar and vision can have difficulty detecting pedestrians in scattered environments, and radar can be used to overcome this problem. Therefore, the motivation of this work is to explore, as a preliminary step, the feasibility of fusing lidar, radar, and RGB for pedestrian detection that potentially can be used for autonomous driving that uses a fully connected convolutional neural network architecture for multimodal sensors. The core of the network is based on SegNet, a pixel-wise semantic segmentation network. In this context, lidar and radar were incorporated by transforming them from 3D pointclouds into 2D gray images with 16-bit depths, and RGB images were incorporated with three channels. The proposed architecture uses a single SegNet for each sensor reading, and the outputs are then applied to a fully connected neural network to fuse the three modalities of sensors. Afterwards, an up-sampling network is applied to recover the fused data. Additionally, a custom dataset of 60 images was proposed for training the architecture, with an additional 10 for evaluation and 10 for testing, giving a total of 80 images. The experiment results show a training mean pixel accuracy of 99.7% and a training mean intersection over union of 99.5%. Also, the testing mean of the IoU was 94.4%, and the testing pixel accuracy was 96.2%. These metric results have successfully demonstrated the effectiveness of using semantic segmentation for pedestrian detection under the modalities of three sensors. Despite some overfitting in the model during experimentation, it performed well in detecting people in test mode. Therefore, it is worth emphasizing that the focus of this work is to show that this method is feasible to be used, as it works regardless of the size of the dataset. Also, a bigger dataset would be necessary to achieve a more appropiate training. This method gives the advantage of detecting pedestrians as the human eye does, thereby resulting in less ambiguity. Additionally, this work has also proposed an extrinsic calibration matrix method for sensor alignment between radar and lidar based on singular value decomposition.
Remote Sensing Image Compression Based on the Multiple Prior Information

Learned image compression has achieved a series of breakthroughs for nature images, but there is little literature focusing on high-resolution remote sensing image (HRRSI) datasets. This paper focuses on designing a learned lossy image compression framework for compressing HRRSIs. Considering the local and non-local redundancy contained in HRRSI, a mixed hyperprior network is designed to explore both the local and non-local redundancy in order to improve the accuracy of entropy estimation. In detail, a transformer-based hyperprior and a CNN-based hyperprior are fused for entropy estimation. Furthermore, to reduce the mismatch between training and testing, a three-stage training strategy is introduced to refine the network. In this training strategy, the entire network is first trained, and then some sub-networks are fixed while the others are trained. To evaluate the effectiveness of the proposed compression algorithm, the experiments are conducted on an HRRSI dataset. The results show that the proposed algorithm achieves comparable or better compression performance than some traditional and learned image compression algorithms, such as Joint Photographic Experts Group (JPEG) and JPEG2000. At a similar or lower bitrate, the proposed algorithm is about 2 dB higher than the PSNR value of JPEG2000.
Cyber expert feedback: Experiences, expectations, and opinions about cyber deception

Human behavior is a critical, yet under-studied topic in cybersecurity. Among the techniques used for cyber defense, for cyber deception, a better understanding of human perception and behavior is crucial. To this point, the current study used a custom Deception Questionnaire to assess perceptions of cyber deception among experts. In this paper, we use a qualitative thematic analysis over the questionnaire responses, which provides a rich insight into the decision-making process of cyber attackers. Results reveal that experts were unlikely to expect deception, and upon encountering potential deception, a subset of attackers would increase and others decrease their attack activity in response. This work augments prior qualitative analyses indicating that cyber deception creates a psychological impact and behavioral change in cyber attackers. Results support the psychological impact and efficacy of deception to deter malicious actors and provide insights about how defenders might utilize deceptive strategies.
Image lossless compression algorithm optimization and FPGA implementation

In this thesis, a minimum redundant prefix coding with higher compression ratio and lower time complexity is proposed for lossless compression of HD images. The compression algorithm is based on Canonical Huffman coding, preprocesses the data source to be compressed according to the image data features, and then compresses the data in batches using the locally uneven features in the data, which improves the compression ratio by 1.678 times compared with the traditional canonical Huffman coding. During the implementation of the algorithm, the counting sorting method with lower time complexity and the code-length table construction method without relying on binary trees are used to reduce the complexity of the algorithm and achieve the purpose of real-time data processing by the system. Finally, the proposed compression algorithm is deployed in FPGA to improve the encoding rate by parallel hardware circuit and pipeline design.
CPSS-FAT: A consistent positive sample selection for object detection with full adaptive threshold

Recent CNN-based methods for object detection largely focus on training the backbone of the object detector, neglecting the key part of selecting positive/negative samples. Instead, based on our analyses about the limitations and inconsistency of existing positive sample selection, we propose a novel Consistent Positive Sample Selection (CPSS) method to select the positive samples automatically, which joints Box-IoU and normalized central distance of object-anchor by mutual weighting. Meanwhile, we employ a consistent approach to location quality evaluation for suppressing the false-positive predicted box when inferencing. Furthermore, an auxiliary Full Adaptive Threshold (FAT) post-processing is also adopted according to the objects' occlusion level to improve the recall ratio. We implement the proposed CPSS-FAT detector using MS COCO 2017 and CityScapes datasets, the comparing results indicate that our approaches are effective and robust to the different objects in an open world. Especially, we achieve 52.2% AP and 43.1% ARS, outperforming most existing detectors.
Smooth and semi-smooth pinball twin support vector machine

In this paper, we firstly propose a new binary classifier termed as smooth pinball twin support vector machine (SP-TSVM) based on a smooth and everywhere differentiable L2-norm pinball loss. It is closely related to the quantile distance, making the model more robust. The SP-TSVM is less sensitive to feature noise, especially noise located near the decision hyperplane. It not only follows the maximal margin principle, but also avoids the matrix inverse operation. Secondly, since SP-TSVM does not have sparsity, we further propose a semi-smooth L2-norm pinball loss function and establish the model: semi-smooth pinball twin support vector machine (SSP-TSVM). It not only has sparsity and inherits the advantages of SP-TSVM, but also can suppress the negative effects of outliers. Since the SSP-TSVM model is a non-convex optimization problem, this paper adopts the convenient and easy-to-use concave–convex procedure (CCCP) optimization method to solve it. In each step of the iterative process, the model solves a series of SP-TSVM-like problems. Experimental results on 19 data sets indicate the validity of our proposed models.
SRDPR: Social Relation-driven Dynamic network for Personalized micro-video Recommendation

With the rapid popularization of the mobile Internet, micro-video has become the mainstream media form today. Compared with traditional video, micro-videos have the characteristics of huge volume, complex video types, and changing user interests. However, micro-video recommendation faces the huge challenge of sparse interaction data, which makes traditional collaborative filtering-based recommendation methods unsuitable for a micro-video recommendation. In addition, only considering the user’s historical behavior for the recommendation while ignoring the user’s social relationship will lead to the problem of a monotonous and cold-start of micro-video recommendation results. To solve the above address, we propose a Social Relation-driven Dynamic network for Personalized micro-video Recommendation, named SRDPR. Specifically, SRDPR divides interest groups by mining users’ public preferences, so active users within the same group can expand user and micro-video interactions. Meanwhile, to perceive users’ dynamic preferences, SRDPR combines a timeline and self-attention mechanism to capture users’ long- and short-term interests and behaviors. Extensive experiments on publicly available datasets show that SRDPR achieves considerable gains over state-of-the-art methods.
Incremental particle swarm optimization for large-scale dynamic optimization with changing variable interactions

Cooperative coevolutionary algorithms have been developed for large-scale dynamic optimization problems via divide-and-conquer mechanisms. Interacting decision variables are divided into the same subproblem for optimization. Their performance greatly depends on problem decomposition and response abilities to environmental changes. However, existing algorithms usually adopt offline decomposition and hence are insufficient to adapt to changes in the underlying interaction structure of decision variables. Quick online decomposition then becomes a crucial issue, along with solution reconstruction for new subproblems. This paper proposes incremental particle swarm optimization to address the two issues. In the proposed method, the incremental differential grouping obtains accurate groupings by iteratively performing edge contractions on the interaction graph of historical groups. A recombination-based sampling strategy is developed to generate high-quality solutions from historical solutions for new subproblems. In order to coordinate with the multimodal property of the problem, swarms are restarted after convergence to search for multiple high-quality solutions. Experimental results on problem instances up to 1000-D show the superiority of the proposed method to state-of-the-art algorithms in terms of solution optimality. The incremental differential grouping can obtain accurate groupings using less function evaluations.
Castles Built on Sand: Observations from Classifying Academic Cybersecurity Datasets with Minimalist Methods

Machine learning (ML) has been a staple of academic research into pattern recognition in many fields, including cybersecurity.The momentum of ML continues to speed up alongside the advances in hardware capabilities and the methods they unlock, primarily (deep) neural networks.However, this article aims to demonstrate that the non-judicious use of ML in two prominent domains of data-based cybersecurity consistently misleads researchers into believing that their proposed methods constitute actual improvements.Armed with 17 stateof-the-art datasets in traffic and malware classification and the simplest possible machine learning model this article will show that the lack of variability in most of these datasets immediately leads to excellent models, even if that model is only one comparison per feature. INTRODUCTIONThe term pattern recognition has largely become synonymous with machine learning, especially in academic research.While in itself this is not a bad direction since these methods have proven capable of solving hitherto unsolvable tasks, eager adoption of increasingly complex ML models in security-oriented pattern recognition tasks presents a problem.That problem is evaluating the true merit of these newly proposed complex models on datasets where the margins at the top are very narrow.While model authors claim the superiority of their novel models, the reality is that it is not possible to rank-order them effectively because extremely simple ML models often score equally well.This article's chief contribution is the continued affirmation of the observation made by (Holte, 1993).Within data-based cybersecurity, even the simplest ML models perform well or even perfectly on the available datasets.This observation from experiment severely undercuts novel ML models' claims that they significantly improve detection.
Rapid detection of white blood cells using hyperspectral microscopic imaging system combined with Multi-data Faster RCNN

Blood test is a basic medical test item, and the content of white blood cells (WBCs) is directly related to the evaluation of the patient's health and the judgment of serious diseases. However, both manual detection method and traditional image recognition method have the problem of insufficient efficiency and accuracy. The purpose of this study is to propose a fast and accurate WBC detection method, called hyperspectral based Multi-data Faster RCNN. For hyperspectral multi-dimensional sensing information, one-dimensional (1-D) CNN and Faster RCNN were used to model and analyze spectral and image features, respectively. And then, the Multi-data Faster RCNN was built, in which the 1-D CNN network and fusion network layer were added in Faster RCNN to realize the joint detection based on spectral and image fusion features of WBCs. The hyperspectral system coupled with Multi-data Faster RCNN can effectively improve the accuracy of WBCs' recognition. The average precision (Ap) value and the F1 score of each classification of WBCs were above 0.9, and the mean average precision (mAP) even reached 0.962. The results showed that the Multi-data Faster RCNN integrated with hyperspectral imaging had great potential in the detection of WBCs.
Question-aware dynamic scene graph of local semantic representation learning for visual question answering

In visual question answering task, it is vital to learn the semantic interactions between the question and target objects in the input image. Existing scene graph-based methods generally extract global features from the image and then perform feature fusion with the question representation. However, the scene graph constructed by these methods only obtains the abstract semantic features from the image, but does not consider the influence of the positional words and semantic information in question. In this paper, we propose a Question-aware Dynamic Scene Graph (QDSG) method. Firstly, we adopt a scene graph of the initial state based on the local attribute features of the image target. Then we design a dynamic scene graph adaptive to different questions based on the initial scene graph, which is used a word-level co-attention mechanism to refine node features and edge features. Finally, iterative reasoning is performed on the refined scene graph and the correct answer is predicted by using the graph attention network model. The proposed method is sufficient to learn the semantic local features to generate the interactive scene graph between the image and question, which is beneficial to the logistic reasoning depending on the adaptive graph refinement. The proposed method outperforms the comparative performance when compared with state-of-the-art models on the GQA dataset and its semantic and structural type datasets.
Stochastic optimization algorithms for parameter identification of three phase induction motors with experimental verification

Induction motors are widely used in industrial processes owing to their efficiency, adaptability, and low cost. For controlling, analyzing, and solving induction motor problems, the estimation of the equivalent circuit parameters is essential. Recently, numerous metaheuristics have been employed to estimate the induction motor parameters, due to their robustness, simplicity, and rapidity. In this paper, six different metaheuristics are used to estimate the parameters of an induction motor: particle swarm optimization (PSO), stochastic fractal search (SFS), equilibrium optimizer (EO), manta ray foraging optimization (MRFO), chaos game optimization (CGO), and jellyfish search (JS). The estimation accuracy of these metaheuristics is verified by computing the sum of the absolute differences between the measured and equivalent model outputs.Experimental results show that the CGO algorithm can provide a small SAD value of 1.1045, and SFS methods can also yield an acceptable SAD value up to 1.1063.